Modeling In H2O

Supervised

H2OCoxProportionalHazardsEstimator

class h2o.estimators.coxph.H2OCoxProportionalHazardsEstimator(**kwargs)

Bases: h2o.estimators.estimator_base.H2OEstimator

Cox Proportional Hazards

Trains a Cox Proportional Hazards Model (CoxPH) on an H2O dataset.

export_checkpoints_dir

Automatically export generated models to this directory.

Type: str.

Examples:

>>> import tempfile
>>> from os import listdir
>>> heart = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/coxph_test/heart.csv")
>>> predictor = "age"
>>> response = "event"
>>> checkpoints_dir = tempfile.mkdtemp()
>>> coxph = H2OCoxProportionalHazardsEstimator(start_column="start",
...                                            stop_column="stop",
...                                            export_checkpoints_dir=checkpoints_dir)
>>> coxph.train(x=predictor,
...             y=response,
...             training_frame=heart)
>>> len(listdir(checkpoints_dir))

ignored_columns

Names of columns to ignore for training.

Type: List[str].

init

Coefficient starting value.

Type: float (default: 0).

Examples:

>>> heart = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/coxph_test/heart.csv")
>>> predictor = "age"
>>> response = "event"
>>> heart_coxph = H2OCoxProportionalHazardsEstimator(start_column="start",
...                                                  stop_column="stop",
...                                                  init=2.9)
>>> heart_coxph.train(x=predictor,
...                   y=response,
...                   training_frame=heart)
>>> heart_coxph.scoring_history()

interaction_pairs

A list of pairwise (first order) column interactions.

Type: List[tuple].

Examples:

>>> heart = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/coxph_test/heart.csv")
>>> predictor = "age"
>>> response = "event"
>>> interaction_pairs = [("start","stop")]
>>> heart_coxph = H2OCoxProportionalHazardsEstimator(start_column="start",
...                                                  stop_column="stop",
...                                                  interaction_pairs=interaction_pairs)
>>> heart_coxph.train(x=predictor,
...                   y=response,
...                   training_frame=heart)
>>> heart_coxph.scoring_history()

interactions

A list of predictor column indices to interact. All pairwise combinations will be computed for the list.

Type: List[str].

Examples:

>>> heart = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/coxph_test/heart.csv")
>>> predictor = "age"
>>> response = "event"
>>> interactions = ['start','stop']
>>> heart_coxph = H2OCoxProportionalHazardsEstimator(start_column="start",
...                                                  stop_column="stop",
...                                                  interactions=interactions)
>>> heart_coxph.train(x=predictor,
...                   y=response,
...                   training_frame=heart)
>>> heart_coxph.scoring_history()

interactions_only

A list of columns that should only be used to create interactions but should not itself participate in model training.

Type: List[str].

Examples:

>>> heart = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/coxph_test/heart.csv")
>>> predictor = "age"
>>> response = "event"
>>> interactions = ['start','stop']
>>> heart_coxph = H2OCoxProportionalHazardsEstimator(start_column="start",
...                                                  stop_column="stop",
...                                                  interactions_only=interactions)
>>> heart_coxph.train(x=predictor,
...                   y=response,
...                   training_frame=heart)
>>> heart_coxph.scoring_history()

lre_min

Minimum log-relative error.

Type: float (default: 9).

Examples:

>>> heart = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/coxph_test/heart.csv")
>>> predictor = "age"
>>> response = "event"
>>> heart_coxph = H2OCoxProportionalHazardsEstimator(start_column="start",
...                                                  stop_column="stop",
...                                                  lre_min=5)
>>> heart_coxph.train(x=predictor,
...                   y=response,
...                   training_frame=heart)
>>> heart_coxph.scoring_history()

max_iterations

Maximum number of iterations.

Type: int (default: 20).

Examples:

>>> heart = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/coxph_test/heart.csv")
>>> predictor = "age"
>>> response = "event"
>>> heart_coxph = H2OCoxProportionalHazardsEstimator(start_column="start",
...                                                  stop_column="stop",
...                                                  max_iterations=50)
>>> heart_coxph.train(x=predictor,
...                   y=response,
...                   training_frame=heart)
>>> heart_coxph.scoring_history()

offset_column

Offset column. This will be added to the combination of columns before applying the link function.

Type: str.

Examples:

>>> heart = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/coxph_test/heart.csv")
>>> predictor = "age"
>>> response = "event"
>>> heart_coxph = H2OCoxProportionalHazardsEstimator(start_column="start",
...                                                  stop_column="stop",
...                                                  offset_column="transplant")
>>> heart_coxph.train(x=predictor,
...                   y=response,
...                   training_frame=heart)
>>> heart_coxph.scoring_history()

response_column

Response variable column.

Type: str.

start_column

Start Time Column.

Type: str.

Examples:

>>> heart = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/coxph_test/heart.csv")
>>> predictor = "age"
>>> response = "event"
>>> train, valid = heart.split_frame(ratios=[.8])
>>> heart_coxph = H2OCoxProportionalHazardsEstimator(start_column="start",
...                                                  stop_column="stop")
>>> heart_coxph.train(x=predictor,
...                   y=response,
...                   training_frame=train,
...                   validation_frame=valid)
>>> heart_coxph.scoring_history()

stop_column

Stop Time Column.

Type: str.

Examples:

>>> heart = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/coxph_test/heart.csv")
>>> predictor = "age"
>>> response = "event"
>>> train, valid = heart.split_frame(ratios=[.8])
>>> heart_coxph = H2OCoxProportionalHazardsEstimator(start_column="start",
...                                                  stop_column="stop")
>>> heart_coxph.train(x=predictor,
...                   y=response,
...                   training_frame=train,
...                   validation_frame=valid)
>>> heart_coxph.scoring_history()

stratify_by

List of columns to use for stratification.

Type: List[str].

ties

Method for Handling Ties.

One of: "efron", "breslow" (default: "efron").

Examples:

>>> heart = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/coxph_test/heart.csv")
>>> predictor = "age"
>>> response = "event"
>>> train, valid = heart.split_frame(ratios=[.8])
>>> heart_coxph = H2OCoxProportionalHazardsEstimator(start_column="start",
...                                                  stop_column="stop",
...                                                  ties="breslow")
>>> heart_coxph.train(x=predictor,
...                   y=response,
...                   training_frame=train,
...                   validation_frame=valid)
>>> heart_coxph.scoring_history()

training_frame

Id of the training data frame.

Type: H2OFrame.

Examples:

>>> heart = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/coxph_test/heart.csv")
>>> predictor = "age"
>>> response = "event"
>>> train, valid = heart.split_frame(ratios=[.8])
>>> heart_coxph = H2OCoxProportionalHazardsEstimator(start_column="start",
...                                                  stop_column="stop")
>>> heart_coxph.train(x=predictor,
...                   y=response,
...                   training_frame=train,
...                   validation_frame=valid)
>>> heart_coxph.scoring_history()

use_all_factor_levels

(Internal. For development only!) Indicates whether to use all factor levels.

Type: bool (default: False).

Examples:

>>> heart = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/coxph_test/heart.csv")
>>> predictor = "age"
>>> response = "event"
>>> heart_coxph = H2OCoxProportionalHazardsEstimator(start_column="start",
...                                                  stop_column="stop",
...                                                  use_all_factor_levels=True)
>>> heart_coxph.train(x=predictor,
...                   y=response,
...                   training_frame=heart)
>>> heart_coxph.scoring_history()

weights_column

Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor.

Type: str.

H2ODeepLearningEstimator

class h2o.estimators.deeplearning.H2ODeepLearningEstimator(**kwargs)

Bases: h2o.estimators.estimator_base.H2OEstimator

Deep Learning

Build a Deep Neural Network model using CPUs Builds a feed-forward multilayer artificial neural network on an H2OFrame

Examples:

>>> from h2o.estimators.deeplearning import H2ODeepLearningEstimator
>>> rows = [[1,2,3,4,0], [2,1,2,4,1], [2,1,4,2,1],
...         [0,1,2,34,1], [2,3,4,1,0]] * 50
>>> fr = h2o.H2OFrame(rows)
>>> fr[4] = fr[4].asfactor()
>>> model = H2ODeepLearningEstimator()
>>> model.train(x=range(4), y=4, training_frame=fr)
>>> model.logloss()

activation

Activation function.

One of: "tanh", "tanh_with_dropout", "rectifier", "rectifier_with_dropout", "maxout", "maxout_with_dropout" (default: "rectifier").

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "cylinders"
>>> cars_dl = H2ODeepLearningEstimator(activation="tanh")
>>> cars_dl.train(x=predictors,
...               y=response,
...               training_frame=train,
...               validation_frame=valid)
>>> cars_dl.mse()

adaptive_rate

Adaptive learning rate.

Type: bool (default: True).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "cylinders"
>>> cars_dl = H2ODeepLearningEstimator(adaptive_rate=True)
>>> cars_dl.train(x=predictors,
...               y=response,
...               training_frame=train,
...               validation_frame=valid)
>>> cars_dl.mse()

autoencoder

Auto-Encoder.

Type: bool (default: False).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "cylinders"
>>> cars_dl = H2ODeepLearningEstimator(autoencoder=True)
>>> cars_dl.train(x=predictors,
...               y=response,
...               training_frame=train,
...               validation_frame=valid)
>>> cars_dl.mse()

average_activation

Average activation for sparse auto-encoder. #Experimental

Type: float (default: 0).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "cylinders"
>>> cars_dl = H2ODeepLearningEstimator(average_activation=1.5,
...                                    seed=1234)
>>> cars_dl.train(x=predictors,
...               y=response,
...               training_frame=train,
...               validation_frame=valid)
>>> cars_dl.mse()

balance_classes

Balance training data class counts via over/under-sampling (for imbalanced data).

Type: bool (default: False).

Examples:

>>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data")
>>> covtype[54] = covtype[54].asfactor()
>>> predictors = covtype.columns[0:54]
>>> response = 'C55'
>>> train, valid = covtype.split_frame(ratios=[.8], seed=1234)
>>> cov_dl = H2ODeepLearningEstimator(balance_classes=True,
...                                   seed=1234)
>>> cov_dl.train(x=predictors,
...              y=response,
...              training_frame=train,
...              validation_frame=valid)
>>> cov_dl.mse()

categorical_encoding

Encoding scheme for categorical features

One of: "auto", "enum", "one_hot_internal", "one_hot_explicit", "binary", "eigen", "label_encoder", "sort_by_response", "enum_limited" (default: "auto").

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> airlines["Year"]= airlines["Year"].asfactor()
>>> airlines["Month"]= airlines["Month"].asfactor()
>>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
>>> airlines["Cancelled"] = airlines["Cancelled"].asfactor()
>>> airlines['FlightNum'] = airlines['FlightNum'].asfactor()
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> response = "IsDepDelayed"
>>> train, valid= airlines.split_frame(ratios=[.8], seed=1234)
>>> encoding = "one_hot_internal"
>>> airlines_dl = H2ODeepLearningEstimator(categorical_encoding=encoding,
...                                        seed=1234)
>>> airlines_dl.train(x=predictors,
...                   y=response,
...                   training_frame=train,
...                   validation_frame=valid)
>>> airlines_dl.mse()

checkpoint

Model checkpoint to resume training with.

Type: str.

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_dl = H2ODeepLearningEstimator(activation="tanh",
...                                    autoencoder=True,
...                                    seed=1234,
...                                    model_id="cars_dl")
>>> cars_dl.train(x=predictors,
...               y=response,
...               training_frame=train,
...               validation_frame=valid)
>>> cars_dl.mse()
>>> cars_cont = H2ODeepLearningEstimator(checkpoint=cars_dl,
...                                      seed=1234)
>>> cars_cont.train(x=predictors,
...                 y=response,
...                 training_frame=train,
...                 validation_frame=valid)
>>> cars_cont.mse()

class_sampling_factors

Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will be automatically computed to obtain class balance during training. Requires balance_classes.

Type: List[float].

Examples:

>>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data")
>>> covtype[54] = covtype[54].asfactor()
>>> predictors = covtype.columns[0:54]
>>> response = 'C55'
>>> train, valid = covtype.split_frame(ratios=[.8], seed=1234)
>>> sample_factors = [1., 0.5, 1., 1., 1., 1., 1.]
>>> cars_dl = H2ODeepLearningEstimator(balance_classes=True,
...                                    class_sampling_factors=sample_factors,
...                                    seed=1234)
>>> cov_dl.train(x=predictors,
...              y=response,
...              training_frame=train,
...              validation_frame=valid)
>>> cov_dl.mse()

classification_stop

Stopping criterion for classification error fraction on training data (-1 to disable).

Type: float (default: 0).

Examples:

>>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data")
>>> covtype[54] = covtype[54].asfactor()
>>> predictors = covtype.columns[0:54]
>>> response = 'C55'
>>> train, valid = covtype.split_frame(ratios=[.8], seed=1234)
>>> cars_dl = H2ODeepLearningEstimator(classification_stop=1.5,
...                                    seed=1234)
>>> cov_dl.train(x=predictors,
...              y=response,
...              training_frame=train,
...              validation_frame=valid)
>>> cov_dl.mse()

col_major

#DEPRECATED Use a column major weight matrix for input layer. Can speed up forward propagation, but might slow down backpropagation.

Type: bool (default: False).

diagnostics

Enable diagnostics for hidden layers.

Type: bool (default: True).

Examples:

>>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data")
>>> covtype[54] = covtype[54].asfactor()
>>> predictors = covtype.columns[0:54]
>>> response = 'C55'
>>> train, valid = covtype.split_frame(ratios=[.8], seed=1234)
>>> cars_dl = H2ODeepLearningEstimator(diagnostics=True,
...                                    seed=1234)  
>>> cov_dl.train(x=predictors,
...              y=response,
...              training_frame=train,
...              validation_frame=valid)
>>> cov_dl.mse()

distribution

Distribution function

One of: "auto", "bernoulli", "multinomial", "gaussian", "poisson", "gamma", "tweedie", "laplace", "quantile", "huber" (default: "auto").

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "cylinders"
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_dl = H2ODeepLearningEstimator(distribution="poisson",
...                                    seed=1234)
>>> cars_dl.train(x=predictors,
...               y=response,
...               training_frame=train,
...               validation_frame=valid)
>>> cars_dl.mse()

elastic_averaging

Elastic averaging between compute nodes can improve distributed model convergence. #Experimental

Type: bool (default: False).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "cylinders"
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_dl = H2ODeepLearningEstimator(elastic_averaging=True,
...                                    seed=1234)
>>> cars_dl.train(x=predictors,
...               y=response,
...               training_frame=train,
...               validation_frame=valid)
>>> cars_dl.mse()

elastic_averaging_moving_rate

Elastic averaging moving rate (only if elastic averaging is enabled).

Type: float (default: 0.9).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "cylinders"
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_dl = H2ODeepLearningEstimator(elastic_averaging_moving_rate=.8,
...                                    seed=1234)
>>> cars_dl.train(x=predictors,
...               y=response,
...               training_frame=train,
...               validation_frame=valid)
>>> cars_dl.mse()

elastic_averaging_regularization

Elastic averaging regularization strength (only if elastic averaging is enabled).

Type: float (default: 0.001).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "cylinders"
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_dl = H2ODeepLearningEstimator(elastic_averaging_regularization=.008,
...                                    seed=1234)
>>> cars_dl.train(x=predictors,
...               y=response,
...               training_frame=train,
...               validation_frame=valid)
>>> cars_dl.mse()

epochs

How many times the dataset should be iterated (streamed), can be fractional.

Type: float (default: 10).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "cylinders"
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_dl = H2ODeepLearningEstimator(epochs=15,
...                                    seed=1234)
>>> cars_dl.train(x=predictors,
...               y=response,
...               training_frame=train,
...               validation_frame=valid)
>>> cars_dl.mse()

epsilon

Adaptive learning rate smoothing factor (to avoid divisions by zero and allow progress).

Type: float (default: 1e-08).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "cylinders"
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_dl = H2ODeepLearningEstimator(epsilon=1e-6,
...                                    seed=1234)
>>> cars_dl.train(x=predictors,
...               y=response,
...               training_frame=train,
...               validation_frame=valid)
>>> cars_dl.mse()

export_checkpoints_dir

Automatically export generated models to this directory.

Type: str.

Examples:

>>> import tempfile
>>> from os import listdir
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "cylinders"
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> checkpoints_dir = tempfile.mkdtemp()
>>> cars_dl = H2ODeepLearningEstimator(export_checkpoints_dir=checkpoints_dir,
...                                    seed=1234)
>>> cars_dl.train(x=predictors,
...               y=response,
...               training_frame=train,
...               validation_frame=valid)
>>> len(listdir(checkpoints_dir))

export_weights_and_biases

Whether to export Neural Network weights and biases to H2O Frames.

Type: bool (default: False).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "cylinders"
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_dl = H2ODeepLearningEstimator(export_weights_and_biases=True,
...                                    seed=1234)
>>> cars_dl.train(x=predictors,
...               y=response,
...               training_frame=train,
...               validation_frame=valid)
>>> cars_dl.mse()

fast_mode

Enable fast mode (minor approximation in back-propagation).

Type: bool (default: True).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "cylinders"
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_dl = H2ODeepLearningEstimator(fast_mode=False,
...                                    seed=1234)          
>>> cars_dl.train(x=predictors,
...               y=response,
...               training_frame=train,
...               validation_frame=valid)
>>> cars_dl.mse()

fold_assignment

Cross-validation fold assignment scheme, if fold_column is not specified. The ‘Stratified’ option will stratify the folds based on the response variable, for classification problems.

One of: "auto", "random", "modulo", "stratified" (default: "auto").

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "cylinders"
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_dl = H2ODeepLearningEstimator(fold_assignment="Random",
...                                    nfolds=5,
...                                    seed=1234)
>>> cars_dl.train(x=predictors,
...               y=response,
...               training_frame=train,
...               validation_frame=valid)
>>> cars_dl.mse()

fold_column

Column with cross-validation fold index assignment per observation.

Type: str.

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "cylinders"
>>> fold_numbers = cars.kfold_column(n_folds=5, seed=1234)
>>> fold_numbers.set_names(["fold_numbers"])
>>> cars = cars.cbind(fold_numbers)
>>> print(cars['fold_numbers'])
>>> cars_dl = H2ODeepLearningEstimator(seed=1234)
>>> cars_dl.train(x=predictors,
...               y=response,
...               training_frame=cars,
...               fold_column="fold_numbers")
>>> cars_dl.mse()

force_load_balance

Force extra load balancing to increase training speed for small datasets (to keep all cores busy).

Type: bool (default: True).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "cylinders"
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_dl = H2ODeepLearningEstimator(force_load_balance=False,
...                                    seed=1234)
>>> cars_dl.train(x=predictors,
...               y=response,
...               training_frame=train,
...               validation_frame=valid)
>>> cars_dl.mse()

hidden

Hidden layer sizes (e.g. [100, 100]).

Type: List[int] (default: [200, 200]).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "cylinders"
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_dl = H2ODeepLearningEstimator(hidden=[100,100],
...                                    seed=1234) 
>>> cars_dl.train(x=predictors,
...               y=response,
...               training_frame=train,
...               validation_frame=valid)
>>> cars_dl.mse()

hidden_dropout_ratios

Hidden layer dropout ratios (can improve generalization), specify one value per hidden layer, defaults to 0.5.

Type: List[float].

Examples:

>>> train = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/bigdata/laptop/mnist/train.csv.gz")
>>> valid = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/bigdata/laptop/mnist/test.csv.gz")
>>> features = list(range(0,784))
>>> target = 784
>>> train[target] = train[target].asfactor()
>>> valid[target] = valid[target].asfactor()
>>> model = H2ODeepLearningEstimator(epochs=20,
...                                  hidden=[200,200],
...                                  hidden_dropout_ratios=[0.5,0.5],
...                                  seed=1234,
...                                  activation='tanhwithdropout')
>>> model.train(x=features,
...             y=target,
...             training_frame=train,
...             validation_frame=valid)
>>> model.mse()

huber_alpha

Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1).

Type: float (default: 0.9).

Examples:

>>> insurance = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/insurance.csv")
>>> predictors = insurance.columns[0:4]
>>> response = 'Claims'
>>> insurance['Group'] = insurance['Group'].asfactor()
>>> insurance['Age'] = insurance['Age'].asfactor()
>>> train, valid = insurance.split_frame(ratios=[.8], seed=1234)
>>> insurance_dl = H2ODeepLearningEstimator(distribution="huber",
...                                         huber_alpha=0.9,
...                                         seed=1234)
>>> insurance_dl.train(x=predictors,
...                    y=response,
...                    training_frame=train,
...                    validation_frame=valid)
>>> insurance_dl.mse()

ignore_const_cols

Ignore constant columns.

Type: bool (default: True).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> cars["const_1"] = 6
>>> cars["const_2"] = 7
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_dl = H2ODeepLearningEstimator(seed=1234,
...                                    ignore_const_cols=True)
>>> cars_dl.train(x=predictors,
...               y=response,
...               training_frame=train,
...               validation_frame=valid)
>>> cars_dl.auc()

ignored_columns

Names of columns to ignore for training.

Type: List[str].

initial_biases

A list of H2OFrame ids to initialize the bias vectors of this model with.

Type: List[H2OFrame].

Examples:

>>> iris = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris.csv")
>>> dl1 = H2ODeepLearningEstimator(hidden=[10,10],
...                                export_weights_and_biases=True)
>>> dl1.train(x=list(range(4)), y=4, training_frame=iris)
>>> p1 = dl1.model_performance(iris).logloss()
>>> ll1 = dl1.predict(iris)
>>> print(p1)
>>> w1 = dl1.weights(0)
>>> w2 = dl1.weights(1)
>>> w3 = dl1.weights(2)
>>> b1 = dl1.biases(0)
>>> b2 = dl1.biases(1)
>>> b3 = dl1.biases(2)
>>> dl2 = H2ODeepLearningEstimator(hidden=[10,10],
...                                initial_weights=[w1, w2, w3],
...                                initial_biases=[b1, b2, b3],
...                                epochs=0)
>>> dl2.train(x=list(range(4)), y=4, training_frame=iris)
>>> dl2.initial_biases

initial_weight_distribution

Initial weight distribution.

One of: "uniform_adaptive", "uniform", "normal" (default: "uniform_adaptive").

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_dl = H2ODeepLearningEstimator(initial_weight_distribution="Uniform",
...                                    seed=1234)
>>> cars_dl.train(x=predictors,
...               y=response,
...               training_frame=train,
...               validation_frame=valid)
>>> cars_dl.auc()

initial_weight_scale

Uniform: -value…value, Normal: stddev.

Type: float (default: 1).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_dl = H2ODeepLearningEstimator(initial_weight_scale=1.5,
...                                    seed=1234) 
>>> cars_dl.train(x=predictors,
...               y=response,
...               training_frame=train,
...               validation_frame=valid)
>>> cars_dl.auc()

initial_weights

A list of H2OFrame ids to initialize the weight matrices of this model with.

Type: List[H2OFrame].

Examples:

>>> iris = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris.csv")
>>> dl1 = H2ODeepLearningEstimator(hidden=[10,10],
...                                export_weights_and_biases=True)
>>> dl1.train(x=list(range(4)), y=4, training_frame=iris)
>>> p1 = dl1.model_performance(iris).logloss()
>>> ll1 = dl1.predict(iris)
>>> print(p1)
>>> w1 = dl1.weights(0)
>>> w2 = dl1.weights(1)
>>> w3 = dl1.weights(2)
>>> b1 = dl1.biases(0)
>>> b2 = dl1.biases(1)
>>> b3 = dl1.biases(2)
>>> dl2 = H2ODeepLearningEstimator(hidden=[10,10],
...                                initial_weights=[w1, w2, w3],
...                                initial_biases=[b1, b2, b3],
...                                epochs=0)
>>> dl2.train(x=list(range(4)), y=4, training_frame=iris)
>>> dl2.initial_weights

input_dropout_ratio

Input layer dropout ratio (can improve generalization, try 0.1 or 0.2).

Type: float (default: 0).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_dl = H2ODeepLearningEstimator(input_dropout_ratio=0.2,
...                                    seed=1234) 
>>> cars_dl.train(x=predictors,
...               y=response,
...               training_frame=train,
...               validation_frame=valid)
>>> cars_dl.auc()

keep_cross_validation_fold_assignment

Whether to keep the cross-validation fold assignment.

Type: bool (default: False).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_dl = H2ODeepLearningEstimator(keep_cross_validation_fold_assignment=True,
...                                    seed=1234)
>>> cars_dl.train(x=predictors,
...               y=response,
...               training_frame=train,
...               validation_frame=valid)
>>> print(cars_dl.cross_validation_fold_assignment())

keep_cross_validation_models

Whether to keep the cross-validation models.

Type: bool (default: True).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_dl = H2ODeepLearningEstimator(keep_cross_validation_models=True,
...                                   seed=1234)
>>> cars_dl.train(x=predictors,
...               y=response,
...               training_frame=train,
...               validation_frame=valid)
>>> print(cars_dl.cross_validation_models())

keep_cross_validation_predictions

Whether to keep the predictions of the cross-validation models.

Type: bool (default: False).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_dl = H2ODeepLearningEstimator(keep_cross_validation_predictions=True,
...                                    seed=1234)
>>> cars_dl.train(x=predictors,
...               y=response,
...               training_frame=train)
>>> print(cars_dl.cross_validation_predictions())

l1

L1 regularization (can add stability and improve generalization, causes many weights to become 0).

Type: float (default: 0).

Examples:

>>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data")
>>> covtype[54] = covtype[54].asfactor()
>>> hh_imbalanced = H2ODeepLearningEstimator(l1=1e-5,
...                                          activation="Rectifier",
...                                          loss="CrossEntropy",
...                                          hidden=[200,200],
...                                          epochs=1,
...                                          balance_classes=False,
...                                          reproducible=True,
...                                          seed=1234)
>>> hh_imbalanced.train(x=list(range(54)),y=54, training_frame=covtype)
>>> hh_imbalanced.mse()

l2

L2 regularization (can add stability and improve generalization, causes many weights to be small.

Type: float (default: 0).

Examples:

>>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data")
>>> covtype[54] = covtype[54].asfactor()
>>> hh_imbalanced = H2ODeepLearningEstimator(l2=1e-5,
...                                          activation="Rectifier",
...                                          loss="CrossEntropy",
...                                          hidden=[200,200],
...                                          epochs=1,
...                                          balance_classes=False,
...                                          reproducible=True,
...                                          seed=1234)
>>> hh_imbalanced.train(x=list(range(54)),y=54, training_frame=covtype)
>>> hh_imbalanced.mse()

loss

Loss function.

One of: "automatic", "cross_entropy", "quadratic", "huber", "absolute", "quantile" (default: "automatic").

Examples:

>>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data")
>>> covtype[54] = covtype[54].asfactor()
>>> hh_imbalanced = H2ODeepLearningEstimator(l1=1e-5,
...                                          activation="Rectifier",
...                                          loss="CrossEntropy",
...                                          hidden=[200,200],
...                                          epochs=1,
...                                          balance_classes=False,
...                                          reproducible=True,
...                                          seed=1234)
>>> hh_imbalanced.train(x=list(range(54)),y=54, training_frame=covtype)
>>> hh_imbalanced.mse()

max_after_balance_size

Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires balance_classes.

Type: float (default: 5).

Examples:

>>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data")
>>> covtype[54] = covtype[54].asfactor()
>>> predictors = covtype.columns[0:54]
>>> response = 'C55'
>>> train, valid = covtype.split_frame(ratios=[.8], seed=1234)
>>> max = .85
>>> cov_dl = H2ODeepLearningEstimator(balance_classes=True,
...                                   max_after_balance_size=max,
...                                   seed=1234)
>>> cov_dl.train(x=predictors,
...              y=response,
...              training_frame=train,
...              validation_frame=valid)
>>> cov_dl.logloss()

max_categorical_features

Max. number of categorical features, enforced via hashing. #Experimental

Type: int (default: 2147483647).

Examples:

>>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data")
>>> covtype[54] = covtype[54].asfactor()
>>> predictors = covtype.columns[0:54]
>>> response = 'C55'
>>> train, valid = covtype.split_frame(ratios=[.8], seed=1234)
>>> cov_dl = H2ODeepLearningEstimator(balance_classes=True,
...                                   max_categorical_features=2147483647,
...                                   seed=1234)
>>> cov_dl.train(x=predictors,
...              y=response,
...              training_frame=train,
...              validation_frame=valid)
>>> cov_dl.logloss()

max_confusion_matrix_size

[Deprecated] Maximum size (# classes) for confusion matrices to be printed in the Logs.

Type: int (default: 20).

max_hit_ratio_k

Max. number (top K) of predictions to use for hit ratio computation (for multi-class only, 0 to disable).

Type: int (default: 0).

Examples:

>>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data")
>>> covtype[54] = covtype[54].asfactor()
>>> predictors = covtype.columns[0:54]
>>> response = 'C55'
>>> train, valid = covtype.split_frame(ratios=[.8], seed=1234)
>>> cov_dl = H2ODeepLearningEstimator(max_hit_ratio_k=3,
...                                   seed=1234) 
>>> cov_dl.train(x=predictors,
...              y=response,
...              training_frame=train,
...              validation_frame=valid)
>>> cov_dl.show()

max_runtime_secs

Maximum allowed runtime in seconds for model training. Use 0 to disable.

Type: float (default: 0).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_dl = H2ODeepLearningEstimator(max_runtime_secs=10,
...                                    seed=1234) 
>>> cars_dl.train(x=predictors,
...               y=response,
...               training_frame=train,
...               validation_frame=valid)
>>> cars_dl.auc()

max_w2

Constraint for squared sum of incoming weights per unit (e.g. for Rectifier).

Type: float (default: 3.4028235e+38).

Examples:

>>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data")
>>> covtype[54] = covtype[54].asfactor()
>>> predictors = covtype.columns[0:54]
>>> response = 'C55'
>>> train, valid = covtype.split_frame(ratios=[.8], seed=1234)
>>> cov_dl = H2ODeepLearningEstimator(activation="RectifierWithDropout",
...                                   hidden=[10,10],
...                                   epochs=10,
...                                   input_dropout_ratio=0.2,
...                                   l1=1e-5,
...                                   max_w2=10.5,
...                                   stopping_rounds=0)
>>> cov_dl.train(x=predictors,
...              y=response,
...              training_frame=train,
...              validation_frame=valid)
>>> cov_dl.mse()

mini_batch_size

Mini-batch size (smaller leads to better fit, larger can speed up and generalize better).

Type: int (default: 1).

Examples:

>>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data")
>>> covtype[54] = covtype[54].asfactor()
>>> predictors = covtype.columns[0:54]
>>> response = 'C55'
>>> train, valid = covtype.split_frame(ratios=[.8], seed=1234)
>>> cov_dl = H2ODeepLearningEstimator(activation="RectifierWithDropout",
...                                   hidden=[10,10],
...                                   epochs=10,
...                                   input_dropout_ratio=0.2,
...                                   l1=1e-5,
...                                   max_w2=10.5,
...                                   stopping_rounds=0)
...                                   mini_batch_size=35
>>> cov_dl.train(x=predictors,
...              y=response,
...              training_frame=train,
...              validation_frame=valid)
>>> cov_dl.mse()

missing_values_handling

Handling of missing values. Either MeanImputation or Skip.

One of: "mean_imputation", "skip" (default: "mean_imputation").

Examples:

>>> boston = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv")
>>> predictors = boston.columns[:-1]
>>> response = "medv"
>>> boston['chas'] = boston['chas'].asfactor()
>>> boston.insert_missing_values()
>>> train, valid = boston.split_frame(ratios=[.8])
>>> boston_dl = H2ODeepLearningEstimator(missing_values_handling="skip")
>>> boston_dl.train(x=predictors,
...                 y=response,
...                 training_frame=train,
...                 validation_frame=valid)
>>> boston_dl.mse()

momentum_ramp

Number of training samples for which momentum increases.

Type: float (default: 1000000).

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> predictors = ["Year","Month","DayofMonth","DayOfWeek","CRSDepTime",
...               "CRSArrTime","UniqueCarrier","FlightNum"]
>>> response_col = "IsDepDelayed"
>>> airlines_dl = H2ODeepLearningEstimator(hidden=[200,200],
...                                        activation="Rectifier",
...                                        input_dropout_ratio=0.0,
...                                        momentum_start=0.9,
...                                        momentum_stable=0.99,
...                                        momentum_ramp=1e7,
...                                        epochs=100,
...                                        stopping_rounds=4,
...                                        train_samples_per_iteration=30000,
...                                        mini_batch_size=32,
...                                        score_duty_cycle=0.25,
...                                        score_interval=1)
>>> airlines_dl.train(x=predictors,
...                   y=response_col,
...                   training_frame=airlines)
>>> airlines_dl.mse()

momentum_stable

Final momentum after the ramp is over (try 0.99).

Type: float (default: 0).

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> predictors = ["Year","Month","DayofMonth","DayOfWeek","CRSDepTime",
...               "CRSArrTime","UniqueCarrier","FlightNum"]
>>> response_col = "IsDepDelayed"
>>> airlines_dl = H2ODeepLearningEstimator(hidden=[200,200],
...                                        activation="Rectifier",
...                                        input_dropout_ratio=0.0,
...                                        momentum_start=0.9,
...                                        momentum_stable=0.99,
...                                        momentum_ramp=1e7,
...                                        epochs=100,
...                                        stopping_rounds=4,
...                                        train_samples_per_iteration=30000,
...                                        mini_batch_size=32,
...                                        score_duty_cycle=0.25,
...                                        score_interval=1)
>>> airlines_dl.train(x=predictors,
...                   y=response_col,
...                   training_frame=airlines)
>>> airlines_dl.mse()

momentum_start

Initial momentum at the beginning of training (try 0.5).

Type: float (default: 0).

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> predictors = ["Year","Month","DayofMonth","DayOfWeek","CRSDepTime",
...               "CRSArrTime","UniqueCarrier","FlightNum"]
>>> response_col = "IsDepDelayed"
>>> airlines_dl = H2ODeepLearningEstimator(hidden=[200,200],
...                                        activation="Rectifier",
...                                        input_dropout_ratio=0.0,
...                                        momentum_start=0.9,
...                                        momentum_stable=0.99,
...                                        momentum_ramp=1e7,
...                                        epochs=100,
...                                        stopping_rounds=4,
...                                        train_samples_per_iteration=30000,
...                                        mini_batch_size=32,
...                                        score_duty_cycle=0.25,
...                                        score_interval=1)
>>> airlines_dl.train(x=predictors,
...                   y=response_col,
...                   training_frame=airlines)
>>> airlines_dl.mse()

nesterov_accelerated_gradient

Use Nesterov accelerated gradient (recommended).

Type: bool (default: True).

Examples:

>>> train = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/bigdata/laptop/mnist/train.csv.gz")
>>> test = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/bigdata/laptop/mnist/test.csv.gz")
>>> predictors = list(range(0,784))
>>> resp = 784
>>> train[resp] = train[resp].asfactor()
>>> test[resp] = test[resp].asfactor()
>>> nclasses = train[resp].nlevels()[0]
>>> model = H2ODeepLearningEstimator(activation="RectifierWithDropout",
...                                  adaptive_rate=False,
...                                  rate=0.01,
...                                  rate_decay=0.9,
...                                  rate_annealing=1e-6,
...                                  momentum_start=0.95,
...                                  momentum_ramp=1e5,
...                                  momentum_stable=0.99,
...                                  nesterov_accelerated_gradient=False,
...                                  input_dropout_ratio=0.2,
...                                  train_samples_per_iteration=20000,
...                                  classification_stop=-1,
...                                  l1=1e-5) 
>>> model.train (x=predictors,
...              y=resp,
...              training_frame=train,
...              validation_frame=test)
>>> model.model_performance()

nfolds

Number of folds for K-fold cross-validation (0 to disable or >= 2).

Type: int (default: 0).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> cars_dl = H2ODeepLearningEstimator(nfolds=5, seed=1234)
>>> cars_dl.train(x=predictors,
...               y=response,
...               training_frame=cars)
>>> cars_dl.auc()

offset_column

Offset column. This will be added to the combination of columns before applying the link function.

Type: str.

Examples:

>>> boston = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv")
>>> predictors = boston.columns[:-1]
>>> response = "medv"
>>> boston['chas'] = boston['chas'].asfactor()
>>> boston["offset"] = boston["medv"].log()
>>> train, valid = boston.split_frame(ratios=[.8], seed=1234)
>>> boston_dl = H2ODeepLearningEstimator(offset_column="offset",
...                                      seed=1234)
>>> boston_dl.train(x=predictors,
...                 y=response,
...                 training_frame=train,
...                 validation_frame=valid)
>>> boston_dl.mse()

overwrite_with_best_model

If enabled, override the final model with the best model found during training.

Type: bool (default: True).

Examples:

>>> boston = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv")
>>> predictors = boston.columns[:-1]
>>> response = "medv"
>>> boston['chas'] = boston['chas'].asfactor()
>>> boston["offset"] = boston["medv"].log()
>>> train, valid = boston.split_frame(ratios=[.8], seed=1234)
>>> boston_dl = H2ODeepLearningEstimator(overwrite_with_best_model=True,
...                                      seed=1234)
>>> boston_dl.train(x=predictors,
...                 y=response,
...                 training_frame=train,
...                 validation_frame=valid)
>>> boston_dl.mse()

pretrained_autoencoder

Pretrained autoencoder model to initialize this model with.

Type: str.

Examples:

>>> from h2o.estimators.deeplearning import H2OAutoEncoderEstimator
>>> resp = 784
>>> nfeatures = 20
>>> train = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/bigdata/laptop/mnist/train.csv.gz")
>>> test = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/bigdata/laptop/mnist/test.csv.gz")
>>> train[resp] = train[resp].asfactor()
>>> test[resp] = test[resp].asfactor()
>>> sid = train[0].runif(0)
>>> train_unsupervised = train[sid>=0.5]
>>> train_unsupervised.pop(resp)
>>> train_supervised = train[sid<0.5]
>>> ae_model = H2OAutoEncoderEstimator(activation="Tanh",
...                                    hidden=[nfeatures],
...                                    model_id="ae_model",
...                                    epochs=1,
...                                    ignore_const_cols=False,
...                                    reproducible=True,
...                                    seed=1234)
>>> ae_model.train(list(range(resp)), training_frame=train_unsupervised)
>>> ae_model.mse()
>>> pretrained_model = H2ODeepLearningEstimator(activation="Tanh",
...                                             hidden=[nfeatures],
...                                             epochs=1,
...                                             reproducible = True,
...                                             seed=1234,
...                                             ignore_const_cols=False,
...                                             pretrained_autoencoder="ae_model")
>>> pretrained_model.train(list(range(resp)), resp,
...                        training_frame=train_supervised,
...                        validation_frame=test)
>>> pretrained_model.mse()

quantile_alpha

Desired quantile for Quantile regression, must be between 0 and 1.

Type: float (default: 0.5).

Examples:

>>> boston = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv")
>>> predictors = boston.columns[:-1]
>>> response = "medv"
>>> boston['chas'] = boston['chas'].asfactor()
>>> train, valid = boston.split_frame(ratios=[.8], seed=1234)
>>> boston_dl = H2ODeepLearningEstimator(distribution="quantile",
...                                      quantile_alpha=.8,
...                                      seed=1234)
>>> boston_dl.train(x=predictors,
...                 y=response,
...                 training_frame=train,
...                 validation_frame=valid)
>>> boston_dl.mse()

quiet_mode

Enable quiet mode for less output to standard output.

Type: bool (default: False).

Examples:

>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv")
>>> titanic['survived'] = titanic['survived'].asfactor()
>>> predictors = titanic.columns
>>> del predictors[1:3]
>>> response = 'survived'
>>> train, valid = titanic.split_frame(ratios=[.8], seed=1234)
>>> titanic_dl = H2ODeepLearningEstimator(quiet_mode=True,
...                                       seed=1234)
>>> titanic_dl.train(x=predictors,
...                  y=response,
...                  training_frame=train,
...                  validation_frame=valid)
>>> titanic_dl.mse()

rate

Learning rate (higher => less stable, lower => slower convergence).

Type: float (default: 0.005).

Examples:

>>> train = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/bigdata/laptop/mnist/train.csv.gz")
>>> test = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/bigdata/laptop/mnist/test.csv.gz")
>>> predictors = list(range(0,784))
>>> resp = 784
>>> train[resp] = train[resp].asfactor()
>>> test[resp] = test[resp].asfactor()
>>> nclasses = train[resp].nlevels()[0]
>>> model = H2ODeepLearningEstimator(activation="RectifierWithDropout",
...                                  adaptive_rate=False,
...                                  rate=0.01,
...                                  rate_decay=0.9,
...                                  rate_annealing=1e-6,
...                                  momentum_start=0.95,
...                                  momentum_ramp=1e5,
...                                  momentum_stable=0.99,
...                                  nesterov_accelerated_gradient=False,
...                                  input_dropout_ratio=0.2,
...                                  train_samples_per_iteration=20000,
...                                  classification_stop=-1,
...                                  l1=1e-5)
>>> model.train (x=predictors,y=resp, training_frame=train, validation_frame=test)
>>> model.model_performance(valid=True)

rate_annealing

Learning rate annealing: rate / (1 + rate_annealing * samples).

Type: float (default: 1e-06).

Examples:

>>> train = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/bigdata/laptop/mnist/train.csv.gz")
>>> test = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/bigdata/laptop/mnist/test.csv.gz")
>>> predictors = list(range(0,784))
>>> resp = 784
>>> train[resp] = train[resp].asfactor()
>>> test[resp] = test[resp].asfactor()
>>> nclasses = train[resp].nlevels()[0]
>>> model = H2ODeepLearningEstimator(activation="RectifierWithDropout",
...                                  adaptive_rate=False,
...                                  rate=0.01,
...                                  rate_decay=0.9,
...                                  rate_annealing=1e-6,
...                                  momentum_start=0.95,
...                                  momentum_ramp=1e5,
...                                  momentum_stable=0.99,
...                                  nesterov_accelerated_gradient=False,
...                                  input_dropout_ratio=0.2,
...                                  train_samples_per_iteration=20000,
...                                  classification_stop=-1,
...                                  l1=1e-5)
>>> model.train (x=predictors,
...              y=resp,
...              training_frame=train,
...              validation_frame=test)
>>> model.mse()

rate_decay

Learning rate decay factor between layers (N-th layer: rate * rate_decay ^ (n - 1).

Type: float (default: 1).

Examples:

>>> train = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/bigdata/laptop/mnist/train.csv.gz")
>>> test = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/bigdata/laptop/mnist/test.csv.gz")
>>> predictors = list(range(0,784))
>>> resp = 784
>>> train[resp] = train[resp].asfactor()
>>> test[resp] = test[resp].asfactor()
>>> nclasses = train[resp].nlevels()[0]
>>> model = H2ODeepLearningEstimator(activation="RectifierWithDropout",
...                                  adaptive_rate=False,
...                                  rate=0.01,
...                                  rate_decay=0.9,
...                                  rate_annealing=1e-6,
...                                  momentum_start=0.95,
...                                  momentum_ramp=1e5,
...                                  momentum_stable=0.99,
...                                  nesterov_accelerated_gradient=False,
...                                  input_dropout_ratio=0.2,
...                                  train_samples_per_iteration=20000,
...                                  classification_stop=-1,
...                                  l1=1e-5)
>>> model.train (x=predictors,
...              y=resp,
...              training_frame=train,
...              validation_frame=test)
>>> model.model_performance()

regression_stop

Stopping criterion for regression error (MSE) on training data (-1 to disable).

Type: float (default: 1e-06).

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> airlines["Year"]= airlines["Year"].asfactor()
>>> airlines["Month"]= airlines["Month"].asfactor()
>>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
>>> airlines["Cancelled"] = airlines["Cancelled"].asfactor()
>>> airlines['FlightNum'] = airlines['FlightNum'].asfactor()
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> response = "IsDepDelayed"
>>> train, valid= airlines.split_frame(ratios=[.8], seed=1234)
>>> airlines_dl = H2ODeepLearningEstimator(regression_stop=1e-6,
...                                        seed=1234)
>>> airlines_dl.train(x=predictors,
...                   y=response,
...                   training_frame=train,
...                   validation_frame=valid)
>>> airlines_dl.auc()

replicate_training_data

Replicate the entire training dataset onto every node for faster training on small datasets.

Type: bool (default: True).

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> airlines["Year"]= airlines["Year"].asfactor()
>>> airlines["Month"]= airlines["Month"].asfactor()
>>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
>>> airlines["Cancelled"] = airlines["Cancelled"].asfactor()
>>> airlines['FlightNum'] = airlines['FlightNum'].asfactor()
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> response = "IsDepDelayed"
>>> airlines_dl = H2ODeepLearningEstimator(replicate_training_data=False)
>>> airlines_dl.train(x=predictors,
...                   y=response,
...                   training_frame=airlines) 
>>> airlines_dl.auc()

reproducible

Force reproducibility on small data (will be slow - only uses 1 thread).

Type: bool (default: False).

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> airlines["Year"]= airlines["Year"].asfactor()
>>> airlines["Month"]= airlines["Month"].asfactor()
>>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
>>> airlines["Cancelled"] = airlines["Cancelled"].asfactor()
>>> airlines['FlightNum'] = airlines['FlightNum'].asfactor()
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> response = "IsDepDelayed"
>>> train, valid= airlines.split_frame(ratios=[.8], seed=1234)
>>> airlines_dl = H2ODeepLearningEstimator(reproducible=True)
>>> airlines_dl.train(x=predictors,
...                   y=response,
...                   training_frame=train,
...                   validation_frame=valid)
>>> airlines_dl.auc()

response_column

Response variable column.

Type: str.

rho

Adaptive learning rate time decay factor (similarity to prior updates).

Type: float (default: 0.99).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> cars_dl = H2ODeepLearningEstimator(rho=0.9,
...                                    seed=1234)
>>> cars_dl.train(x=predictors,
...               y=response,
...               training_frame=cars)
>>> cars_dl.auc()

score_duty_cycle

Maximum duty cycle fraction for scoring (lower: more training, higher: more scoring).

Type: float (default: 0.1).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> cars_dl = H2ODeepLearningEstimator(score_duty_cycle=0.2,
...                                    seed=1234)
>>> cars_dl.train(x=predictors,
...               y=response,
...               training_frame=cars)
>>> cars_dl.auc()

score_each_iteration

Whether to score during each iteration of model training.

Type: bool (default: False).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> cars_dl = H2ODeepLearningEstimator(score_each_iteration=True,
...                                    seed=1234) 
>>> cars_dl.train(x=predictors,
...               y=response,
...               training_frame=cars)
>>> cars_dl.auc()

score_interval

Shortest time interval (in seconds) between model scoring.

Type: float (default: 5).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> cars_dl = H2ODeepLearningEstimator(score_interval=3,
...                                    seed=1234) 
>>> cars_dl.train(x=predictors,
...               y=response,
...               training_frame=cars)
>>> cars_dl.auc()

score_training_samples

Number of training set samples for scoring (0 for all).

Type: int (default: 10000).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> cars_dl = H2ODeepLearningEstimator(score_training_samples=10000,
...                                    seed=1234)
>>> cars_dl.train(x=predictors,
...               y=response,
...               training_frame=cars)
>>> cars_dl.auc()

score_validation_samples

Number of validation set samples for scoring (0 for all).

Type: int (default: 0).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_dl = H2ODeepLearningEstimator(score_validation_samples=3,
...                                    seed=1234)
>>> cars_dl.train(x=predictors,
...               y=response,
...               training_frame=train,
...               validation_frame=valid)
>>> cars_dl.auc()

score_validation_sampling

Method used to sample validation dataset for scoring.

One of: "uniform", "stratified" (default: "uniform").

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_dl = H2ODeepLearningEstimator(score_validation_sampling="uniform",
...                                    seed=1234)
>>> cars_dl.train(x=predictors,
...               y=response,
...               training_frame=train,
...               validation_frame=valid)
>>> cars_dl.auc()

seed

Seed for random numbers (affects sampling) - Note: only reproducible when running single threaded.

Type: int (default: -1).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_dl = H2ODeepLearningEstimator(seed=1234)
>>> cars_dl.train(x=predictors,
...               y=response,
...               training_frame=train,
...               validation_frame=valid)
>>> cars_dl.auc()

shuffle_training_data

Enable shuffling of training data (recommended if training data is replicated and train_samples_per_iteration is close to #nodes x #rows, of if using balance_classes).

Type: bool (default: False).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_dl = H2ODeepLearningEstimator(shuffle_training_data=True,
...                                    seed=1234)
>>> cars_dl.train(x=predictors,
...               y=response,
...               training_frame=cars)
>>> cars_dl.auc()

single_node_mode

Run on a single node for fine-tuning of model parameters.

Type: bool (default: False).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_dl = H2ODeepLearningEstimator(single_node_mode=True,
...                                    seed=1234) 
>>> cars_dl.train(x=predictors,
...               y=response,
...               training_frame=cars)
>>> cars_dl.auc()

sparse

Sparse data handling (more efficient for data with lots of 0 values).

Type: bool (default: False).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_dl = H2ODeepLearningEstimator(sparse=True,
...                                    seed=1234)
>>> cars_dl.train(x=predictors,
...               y=response,
...               training_frame=cars)
>>> cars_dl.auc()

sparsity_beta

Sparsity regularization. #Experimental

Type: float (default: 0).

Examples:

>>> from h2o.estimators import H2OAutoEncoderEstimator
>>> resp = 784
>>> nfeatures = 20
>>> train = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/bigdata/laptop/mnist/train.csv.gz")
>>> test = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/bigdata/laptop/mnist/test.csv.gz")
>>> train[resp] = train[resp].asfactor()
>>> test[resp] = test[resp].asfactor()
>>> sid = train[0].runif(0)
>>> train_unsupervised = train[sid>=0.5]
>>> train_unsupervised.pop(resp)
>>> ae_model = H2OAutoEncoderEstimator(activation="Tanh",
...                                    hidden=[nfeatures],
...                                    epochs=1,
...                                    ignore_const_cols=False,
...                                    reproducible=True,
...                                    sparsity_beta=0.5,
...                                    seed=1234)
>>> ae_model.train(list(range(resp)),
...                training_frame=train_unsupervised)
>>> ae_model.mse()

standardize

If enabled, automatically standardize the data. If disabled, the user must provide properly scaled input data.

Type: bool (default: True).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> cars_dl = H2ODeepLearningEstimator(standardize=True,
...                                    seed=1234)
>>> cars_dl.train(x=predictors,
...               y=response,
...               training_frame=cars)
>>> cars_dl.auc()

stopping_metric

Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anonomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client.

One of: "auto", "deviance", "logloss", "mse", "rmse", "mae", "rmsle", "auc", "aucpr", "lift_top_group", "misclassification", "mean_per_class_error", "custom", "custom_increasing" (default: "auto").

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> airlines["Year"]= airlines["Year"].asfactor()
>>> airlines["Month"]= airlines["Month"].asfactor()
>>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
>>> airlines["Cancelled"] = airlines["Cancelled"].asfactor()
>>> airlines['FlightNum'] = airlines['FlightNum'].asfactor()
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> response = "IsDepDelayed"
>>> train, valid= airlines.split_frame(ratios=[.8], seed=1234)
>>> airlines_dl = H2ODeepLearningEstimator(stopping_metric="auc",
...                                        stopping_rounds=3,
...                                        stopping_tolerance=1e-2,
...                                        seed=1234)
>>> airlines_dl.train(x=predictors,
...                   y=response,
...                   training_frame=train,
...                   validation_frame=valid)
>>> airlines_dl.auc()

stopping_rounds

Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable)

Type: int (default: 5).

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> airlines["Year"]= airlines["Year"].asfactor()
>>> airlines["Month"]= airlines["Month"].asfactor()
>>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
>>> airlines["Cancelled"] = airlines["Cancelled"].asfactor()
>>> airlines['FlightNum'] = airlines['FlightNum'].asfactor()
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> response = "IsDepDelayed"
>>> train, valid= airlines.split_frame(ratios=[.8], seed=1234)
>>> airlines_dl = H2ODeepLearningEstimator(stopping_metric="auc",
...                                        stopping_rounds=3,
...                                        stopping_tolerance=1e-2,
...                                        seed=1234)
>>> airlines_dl.train(x=predictors,
...                   y=response,
...                   training_frame=train,
...                   validation_frame=valid)
>>> airlines_dl.auc()

stopping_tolerance

Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much)

Type: float (default: 0).

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> airlines["Year"]= airlines["Year"].asfactor()
>>> airlines["Month"]= airlines["Month"].asfactor()
>>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
>>> airlines["Cancelled"] = airlines["Cancelled"].asfactor()
>>> airlines['FlightNum'] = airlines['FlightNum'].asfactor()
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> response = "IsDepDelayed"
>>> train, valid= airlines.split_frame(ratios=[.8], seed=1234)
>>> airlines_dl = H2ODeepLearningEstimator(stopping_metric="auc",
...                                        stopping_rounds=3,
...                                        stopping_tolerance=1e-2,
...                                        seed=1234)
>>> airlines_dl.train(x=predictors,
...                   y=response,
...                   training_frame=train,
...                   validation_frame=valid)
>>> airlines_dl.auc()

target_ratio_comm_to_comp

Target ratio of communication overhead to computation. Only for multi-node operation and train_samples_per_iteration = -2 (auto-tuning).

Type: float (default: 0.05).

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> airlines["Year"]= airlines["Year"].asfactor()
>>> airlines["Month"]= airlines["Month"].asfactor()
>>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
>>> airlines["Cancelled"] = airlines["Cancelled"].asfactor()
>>> airlines['FlightNum'] = airlines['FlightNum'].asfactor()
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> response = "IsDepDelayed"
>>> train, valid= airlines.split_frame(ratios=[.8], seed=1234)
>>> airlines_dl = H2ODeepLearningEstimator(target_ratio_comm_to_comp=0.05,
...                                        seed=1234)
>>> airlines_dl.train(x=predictors,
...                   y=response,
...                   training_frame=train,
...                   validation_frame=valid)
>>> airlines_dl.auc()

train_samples_per_iteration

Number of training samples (globally) per MapReduce iteration. Special values are 0: one epoch, -1: all available data (e.g., replicated training data), -2: automatic.

Type: int (default: -2).

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> airlines["Year"]= airlines["Year"].asfactor()
>>> airlines["Month"]= airlines["Month"].asfactor()
>>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
>>> airlines["Cancelled"] = airlines["Cancelled"].asfactor()
>>> airlines['FlightNum'] = airlines['FlightNum'].asfactor()
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> response = "IsDepDelayed"
>>> train, valid= airlines.split_frame(ratios=[.8], seed=1234)
>>> airlines_dl = H2ODeepLearningEstimator(train_samples_per_iteration=-1,
...                                        epochs=1,
...                                        seed=1234)
>>> airlines_dl.train(x=predictors,
...                   y=response,
...                   training_frame=train,
...                   validation_frame=valid)
>>> airlines_dl.auc()

training_frame

Id of the training data frame.

Type: H2OFrame.

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> airlines["Year"]= airlines["Year"].asfactor()
>>> airlines["Month"]= airlines["Month"].asfactor()
>>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
>>> airlines["Cancelled"] = airlines["Cancelled"].asfactor()
>>> airlines['FlightNum'] = airlines['FlightNum'].asfactor()
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> response = "IsDepDelayed"
>>> train, valid= airlines.split_frame(ratios=[.8], seed=1234)
>>> airlines_dl = H2ODeepLearningEstimator()
>>> airlines_dl.train(x=predictors,
...                   y=response,
...                   training_frame=train,
...                   validation_frame=valid)
>>> airlines_dl.auc()

tweedie_power

Tweedie power for Tweedie regression, must be between 1 and 2.

Type: float (default: 1.5).

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> airlines["Year"]= airlines["Year"].asfactor()
>>> airlines["Month"]= airlines["Month"].asfactor()
>>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
>>> airlines["Cancelled"] = airlines["Cancelled"].asfactor()
>>> airlines['FlightNum'] = airlines['FlightNum'].asfactor()
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> response = "IsDepDelayed"
>>> train, valid= airlines.split_frame(ratios=[.8], seed=1234)
>>> airlines_dl = H2ODeepLearningEstimator(tweedie_power=1.5,
...                                        seed=1234) 
>>> airlines_dl.train(x=predictors,
...                   y=response,
...                   training_frame=train,
...                   validation_frame=valid)
>>> airlines_dl.auc()

use_all_factor_levels

Use all factor levels of categorical variables. Otherwise, the first factor level is omitted (without loss of accuracy). Useful for variable importances and auto-enabled for autoencoder.

Type: bool (default: True).

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> airlines["Year"]= airlines["Year"].asfactor()
>>> airlines["Month"]= airlines["Month"].asfactor()
>>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
>>> airlines["Cancelled"] = airlines["Cancelled"].asfactor()
>>> airlines['FlightNum'] = airlines['FlightNum'].asfactor()
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> response = "IsDepDelayed"
>>> train, valid= airlines.split_frame(ratios=[.8], seed=1234)
>>> airlines_dl = H2ODeepLearningEstimator(use_all_factor_levels=True,
...                                        seed=1234)
>>> airlines_dl.train(x=predictors,
...                   y=response,
...                   training_frame=train,
...                   validation_frame=valid)
>>> airlines_dl.mse()

validation_frame

Id of the validation data frame.

Type: H2OFrame.

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_dl = H2ODeepLearningEstimator(standardize=True,
...                                    seed=1234)
>>> cars_dl.train(x=predictors,
...               y=response,
...               training_frame=train,
...               validation_frame=valid)
>>> cars_dl.auc()

variable_importances

Compute variable importances for input features (Gedeon method) - can be slow for large networks.

Type: bool (default: True).

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> airlines["Year"]= airlines["Year"].asfactor()
>>> airlines["Month"]= airlines["Month"].asfactor()
>>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
>>> airlines["Cancelled"] = airlines["Cancelled"].asfactor()
>>> airlines['FlightNum'] = airlines['FlightNum'].asfactor()
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> response = "IsDepDelayed"
>>> train, valid= airlines.split_frame(ratios=[.8], seed=1234)
>>> airlines_dl = H2ODeepLearningEstimator(variable_importances=True,
...                                        seed=1234)
>>> airlines_dl.train(x=predictors,
...                   y=response,
...                   training_frame=train,
...                   validation_frame=valid)
>>> airlines_dl.mse()

weights_column

Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor.

Type: str.

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","acceleration","year"]
>>> response = "economy_20mpg"
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_dl = H2ODeepLearningEstimator(seed=1234)
>>> cars_dl.train(x=predictors,
...               y=response,
...               training_frame=train,
...               validation_frame=valid)
>>> cars_dl.auc()

H2ODeepWaterEstimator

class h2o.estimators.deepwater.H2ODeepWaterEstimator(**kwargs)

Bases: h2o.estimators.estimator_base.H2OEstimator

Deep Water

Build a Deep Learning model using multiple native GPU backends Builds a deep neural network on an H2OFrame containing various data sources

activation

Activation function. Only used if no user-defined network architecture file is provided, and only for problem_type=dataset.

One of: "rectifier", "tanh".

autoencoder

Auto-Encoder.

Type: bool (default: False).

static available()

Ask the H2O server whether a Deep Water model can be built (depends on availability of native backends). :return: True if a deep water model can be built, or False otherwise.

backend

Deep Learning Backend.

One of: "mxnet", "caffe", "tensorflow" (default: "mxnet").

balance_classes

Balance training data class counts via over/under-sampling (for imbalanced data).

Type: bool (default: False).

cache_data

Whether to cache the data in memory (automatically disabled if data size is too large).

Type: bool (default: True).

categorical_encoding

Encoding scheme for categorical features

One of: "auto", "enum", "one_hot_internal", "one_hot_explicit", "binary", "eigen", "label_encoder", "sort_by_response", "enum_limited" (default: "auto").

channels

Number of (color) channels.

Type: int (default: 3).

checkpoint

Model checkpoint to resume training with.

Type: str.

class_sampling_factors

Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will be automatically computed to obtain class balance during training. Requires balance_classes.

Type: List[float].

classification_stop

Stopping criterion for classification error fraction on training data (-1 to disable).

Type: float (default: 0).

clip_gradient

Clip gradients once their absolute value is larger than this value.

Type: float (default: 10).

device_id

Device IDs (which GPUs to use).

Type: List[int] (default: [0]).

distribution

Distribution function

One of: "auto", "bernoulli", "multinomial", "gaussian", "poisson", "gamma", "tweedie", "laplace", "quantile", "huber" (default: "auto").

epochs

How many times the dataset should be iterated (streamed), can be fractional.

Type: float (default: 10).

export_checkpoints_dir

Automatically export generated models to this directory.

Type: str.

export_native_parameters_prefix

Path (prefix) where to export the native model parameters after every iteration.

Type: str.

fold_assignment

Cross-validation fold assignment scheme, if fold_column is not specified. The ‘Stratified’ option will stratify the folds based on the response variable, for classification problems.

One of: "auto", "random", "modulo", "stratified" (default: "auto").

fold_column

Column with cross-validation fold index assignment per observation.

Type: str.

gpu

Whether to use a GPU (if available).

Type: bool (default: True).

hidden

Hidden layer sizes (e.g. [200, 200]). Only used if no user-defined network architecture file is provided, and only for problem_type=dataset.

Type: List[int].

hidden_dropout_ratios

Hidden layer dropout ratios (can improve generalization), specify one value per hidden layer, defaults to 0.5.

Type: List[float].

ignore_const_cols

Ignore constant columns.

Type: bool (default: True).

ignored_columns

Names of columns to ignore for training.

Type: List[str].

image_shape

Width and height of image.

Type: List[int] (default: [0, 0]).

input_dropout_ratio

Input layer dropout ratio (can improve generalization, try 0.1 or 0.2).

Type: float (default: 0).

keep_cross_validation_fold_assignment

Whether to keep the cross-validation fold assignment.

Type: bool (default: False).

keep_cross_validation_models

Whether to keep the cross-validation models.

Type: bool (default: True).

keep_cross_validation_predictions

Whether to keep the predictions of the cross-validation models.

Type: bool (default: False).

learning_rate

Learning rate (higher => less stable, lower => slower convergence).

Type: float (default: 0.001).

learning_rate_annealing

Learning rate annealing: rate / (1 + rate_annealing * samples).

Type: float (default: 1e-06).

max_after_balance_size

Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires balance_classes.

Type: float (default: 5).

max_runtime_secs

Maximum allowed runtime in seconds for model training. Use 0 to disable.

Type: float (default: 0).

mean_image_file

Path of file containing the mean image data for data normalization.

Type: str.

mini_batch_size

Mini-batch size (smaller leads to better fit, larger can speed up and generalize better).

Type: int (default: 32).

momentum_ramp

Number of training samples for which momentum increases.

Type: float (default: 10000).

momentum_stable

Final momentum after the ramp is over (try 0.99).

Type: float (default: 0.9).

momentum_start

Initial momentum at the beginning of training (try 0.5).

Type: float (default: 0.9).

network

Network architecture.

One of: "auto", "user", "lenet", "alexnet", "vgg", "googlenet", "inception_bn", "resnet" (default: "auto").

network_definition_file

Path of file containing network definition (graph, architecture).

Type: str.

network_parameters_file

Path of file containing network (initial) parameters (weights, biases).

Type: str.

nfolds

Number of folds for K-fold cross-validation (0 to disable or >= 2).

Type: int (default: 0).

offset_column

Offset column. This will be added to the combination of columns before applying the link function.

Type: str.

overwrite_with_best_model

If enabled, override the final model with the best model found during training.

Type: bool (default: True).

problem_type

Problem type, auto-detected by default. If set to image, the H2OFrame must contain a string column containing the path (URI or URL) to the images in the first column. If set to text, the H2OFrame must contain a string column containing the text in the first column. If set to dataset, Deep Water behaves just like any other H2O Model and builds a model on the provided H2OFrame (non-String columns).

One of: "auto", "image", "dataset" (default: "auto").

regression_stop

Stopping criterion for regression error (MSE) on training data (-1 to disable).

Type: float (default: 0).

response_column

Response variable column.

Type: str.

score_duty_cycle

Maximum duty cycle fraction for scoring (lower: more training, higher: more scoring).

Type: float (default: 0.1).

score_each_iteration

Whether to score during each iteration of model training.

Type: bool (default: False).

score_interval

Shortest time interval (in seconds) between model scoring.

Type: float (default: 5).

score_training_samples

Number of training set samples for scoring (0 for all).

Type: int (default: 10000).

score_validation_samples

Number of validation set samples for scoring (0 for all).

Type: int (default: 0).

seed

Seed for random numbers (affects sampling) - Note: only reproducible when running single threaded.

Type: int (default: -1).

shuffle_training_data

Enable global shuffling of training data.

Type: bool (default: True).

sparse

Sparse data handling (more efficient for data with lots of 0 values).

Type: bool (default: False).

standardize

If enabled, automatically standardize the data. If disabled, the user must provide properly scaled input data.

Type: bool (default: True).

stopping_metric

Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anonomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client.

One of: "auto", "deviance", "logloss", "mse", "rmse", "mae", "rmsle", "auc", "aucpr", "lift_top_group", "misclassification", "mean_per_class_error", "custom", "custom_increasing" (default: "auto").

stopping_rounds

Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable)

Type: int (default: 5).

stopping_tolerance

Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much)

Type: float (default: 0).

target_ratio_comm_to_comp

Target ratio of communication overhead to computation. Only for multi-node operation and train_samples_per_iteration = -2 (auto-tuning).

Type: float (default: 0.05).

train_samples_per_iteration

Number of training samples (globally) per MapReduce iteration. Special values are 0: one epoch, -1: all available data (e.g., replicated training data), -2: automatic.

Type: int (default: -2).

training_frame

Id of the training data frame.

Type: H2OFrame.

validation_frame

Id of the validation data frame.

Type: H2OFrame.

weights_column

Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor.

Type: str.

H2OGradientBoostingEstimator

class h2o.estimators.gbm.H2OGradientBoostingEstimator(**kwargs)

Bases: h2o.estimators.estimator_base.H2OEstimator

Gradient Boosting Machine

Builds gradient boosted trees on a parsed data set, for regression or classification. The default distribution function will guess the model type based on the response column type. Otherwise, the response column must be an enum for “bernoulli” or “multinomial”, and numeric for all other distributions.

balance_classes

Balance training data class counts via over/under-sampling (for imbalanced data).

Type: bool (default: False).

Examples:

>>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data")
>>> covtype[54] = covtype[54].asfactor()
>>> predictors = covtype.columns[0:54]
>>> response = 'C55'
>>> train, valid = covtype.split_frame(ratios=[.8], seed=1234)
>>> cov_gbm = H2OGradientBoostingEstimator(balance_classes=True,
...                                        seed=1234)
>>> cov_gbm.train(x=predictors,
...               y=response,
...               training_frame=train,
...               validation_frame=valid)
>>> cov_gbm.logloss(valid=True)

build_tree_one_node

Run on one node only; no network overhead but fewer cpus used. Suitable for small datasets.

Type: bool (default: False).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_gbm = H2OGradientBoostingEstimator(build_tree_one_node=True,
...                                         seed=1234)
>>> cars_gbm.train(x=predictors,
...                y=response,
...                training_frame=train,
...                validation_frame=valid)
>>> cars_gbm.auc(valid=True)

calibrate_model

Use Platt Scaling to calculate calibrated class probabilities. Calibration can provide more accurate estimates of class probabilities.

Type: bool (default: False).

Examples:

>>> ecology = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/ecology_model.csv")
>>> ecology['Angaus'] = ecology['Angaus'].asfactor()
>>> response = 'Angaus'
>>> train, calib = ecology.split_frame(seed = 12354)
>>> predictors = ecology.columns[3:13]
>>> w = h2o.create_frame(binary_fraction=1,
...                      binary_ones_fraction=0.5,
...                      missing_fraction=0,
...                      rows=744, cols=1)
>>> w.set_names(["weight"])
>>> train = train.cbind(w)
>>> ecology_gbm = H2OGradientBoostingEstimator(ntrees=10,
...                                            max_depth=5,
...                                            min_rows=10,
...                                            learn_rate=0.1,
...                                            distribution="multinomial",
...                                            weights_column="weight",
...                                            calibrate_model=True,
...                                            calibration_frame=calib)
>>> ecology_gbm.train(x=predictors,
...                   y="Angaus",
...                   training_frame=train)
>>> ecology_gbm.auc()

calibration_frame

Calibration frame for Platt Scaling

Type: H2OFrame.

Examples:

>>> ecology = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/ecology_model.csv")
>>> ecology['Angaus'] = ecology['Angaus'].asfactor()
>>> response = 'Angaus'
>>> predictors = ecology.columns[3:13]
>>> train, calib = ecology.split_frame(seed=12354)
>>> w = h2o.create_frame(binary_fraction=1,
...                      binary_ones_fraction=0.5,
...                      missing_fraction=0,
...                      rows=744,cols=1)
>>> w.set_names(["weight"])
>>> train = train.cbind(w)
>>> ecology_gbm = H2OGradientBoostingEstimator(ntrees=10,
...                                            max_depth=5,
...                                            min_rows=10,
...                                            learn_rate=0.1,
...                                            distribution="multinomial",
...                                            calibrate_model=True,
...                                            calibration_frame=calib)
>>> ecology_gbm.train(x=predictors,
...                   y="Angaus",
...                   training_frame=train,
...                   weights_column="weight")
>>> ecology_gbm.auc()

categorical_encoding

Encoding scheme for categorical features

One of: "auto", "enum", "one_hot_internal", "one_hot_explicit", "binary", "eigen", "label_encoder", "sort_by_response", "enum_limited" (default: "auto").

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> airlines["Year"] = airlines["Year"].asfactor()
>>> airlines["Month"] = airlines["Month"].asfactor()
>>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
>>> airlines["Cancelled"] = airlines["Cancelled"].asfactor()
>>> airlines['FlightNum'] = airlines['FlightNum'].asfactor()
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> response = "IsDepDelayed"
>>> train, valid = airlines.split_frame(ratios=[.8], seed=1234)
>>> airlines_gbm = H2OGradientBoostingEstimator(categorical_encoding="labelencoder",
...                                             seed=1234)
>>> airlines_gbm.train(x=predictors,
...                    y=response,
...                    training_frame=train,
...                    validation_frame=valid)
>>> airlines_gbm.auc(valid=True)

check_constant_response

Check if response column is constant. If enabled, then an exception is thrown if the response column is a constant value.If disabled, then model will train regardless of the response column being a constant value or not.

Type: bool (default: True).

Examples:

>>> train = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/iris/iris_train.csv")
>>> train["constantCol"] = 1
>>> my_gbm = H2OGradientBoostingEstimator(check_constant_response=False)
>>> my_gbm.train(x=list(range(1,5)),
...              y="constantCol",
...              training_frame=train)

checkpoint

Model checkpoint to resume training with.

Type: str.

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_gbm = H2OGradientBoostingEstimator(ntrees=1,
...                                         seed=1234)
>>> cars_gbm.train(x=predictors,
...                y=response,
...                training_frame=train,
...                validation_frame=valid)
>>> print(cars_gbm.auc(valid=True))
>>> print("Number of trees built for cars_gbm model:", cars_gbm.ntrees)
>>> cars_gbm_continued = H2OGradientBoostingEstimator(checkpoint=cars_gbm.model_id,
...                                                   ntrees=50,
...                                                   seed=1234)
>>> cars_gbm_continued.train(x=predictors,
...                          y=response,
...                          training_frame=train,
...                          validation_frame=valid)
>>> cars_gbm_continued.auc(valid=True)
>>> print("Number of trees built for cars_gbm model:",cars_gbm_continued.ntrees) 

class_sampling_factors

Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will be automatically computed to obtain class balance during training. Requires balance_classes.

Type: List[float].

Examples:

>>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data")
>>> covtype[54] = covtype[54].asfactor()
>>> predictors = covtype.columns[0:54]
>>> response = 'C55'
>>> train, valid = covtype.split_frame(ratios=[.8], seed=1234)
>>> sample_factors = [1., 0.5, 1., 1., 1., 1., 1.]
>>> cov_gbm = H2OGradientBoostingEstimator(balance_classes=True,
...                                        class_sampling_factors=sample_factors,
...                                        seed=1234)
>>> cov_gbm.train(x=predictors,
...               y=response,
...               training_frame=train,
...               validation_frame=valid)
>>> cov_gbm.logloss(valid=True)

col_sample_rate

Column sample rate (from 0.0 to 1.0)

Type: float (default: 1).

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> airlines["Year"] = airlines["Year"].asfactor()
>>> airlines["Month"] = airlines["Month"].asfactor()
>>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
>>> airlines["Cancelled"] = airlines["Cancelled"].asfactor()
>>> airlines['FlightNum'] = airlines['FlightNum'].asfactor()
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> response = "IsDepDelayed"
>>> train, valid = airlines.split_frame(ratios=[.8], seed=1234)
>>> airlines_gbm = H2OGradientBoostingEstimator(col_sample_rate=.7,
...                                             seed=1234)
>>> airlines_gbm.train(x=predictors,
...                    y=response,
...                    training_frame=train,
...                    validation_frame=valid)
>>> airlines_gbm.auc(valid=True)

col_sample_rate_change_per_level

Relative change of the column sampling rate for every level (must be > 0.0 and <= 2.0)

Type: float (default: 1).

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> airlines["Year"] = airlines["Year"].asfactor()
>>> airlines["Month"] = airlines["Month"].asfactor()
>>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
>>> airlines["Cancelled"] = airlines["Cancelled"].asfactor()
>>> airlines['FlightNum'] = airlines['FlightNum'].asfactor()
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> response = "IsDepDelayed"
>>> train, valid = airlines.split_frame(ratios=[.8], seed=1234)
>>> airlines_gbm = H2OGradientBoostingEstimator(col_sample_rate_change_per_level=.9,
...                                             seed=1234)
>>> airlines_gbm.train(x=predictors,
...                    y=response,
...                    training_frame=train,
...                    validation_frame=valid)
>>> airlines_gbm.auc(valid=True)

col_sample_rate_per_tree

Column sample rate per tree (from 0.0 to 1.0)

Type: float (default: 1).

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> airlines["Year"] = airlines["Year"].asfactor()
>>> airlines["Month"] = airlines["Month"].asfactor()
>>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
>>> airlines["Cancelled"] = airlines["Cancelled"].asfactor()
>>> airlines['FlightNum'] = airlines['FlightNum'].asfactor()
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> response = "IsDepDelayed"
>>> train, valid = airlines.split_frame(ratios=[.8], seed=1234)
>>> airlines_gbm = H2OGradientBoostingEstimator(col_sample_rate_per_tree=.7,
...                                             seed=1234)
>>> airlines_gbm.train(x=predictors,
...                    y=response,
...                    training_frame=train,
...                    validation_frame=valid)
>>> airlines_gbm.auc(valid=True)

custom_distribution_func

Reference to custom distribution, format: language:keyName=funcName

Type: str.

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> airlines["Year"] = airlines["Year"].asfactor()
>>> airlines["Month"] = airlines["Month"].asfactor()
>>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
>>> airlines["Cancelled"] = airlines["Cancelled"].asfactor()
>>> airlines['FlightNum'] = airlines['FlightNum'].asfactor()
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> response = "IsDepDelayed"
>>> train, valid = airlines.split_frame(ratios=[.8], seed=1234)
>>> airlines_gbm = H2OGradientBoostingEstimator(ntrees=3,
...                                             max_depth=5,
...                                             distribution="bernoulli",
...                                             seed=1234)
>>> airlines_gbm.train(x=predictors,
...                    y=response,
...                    training_frame=train,
...                    validation_frame valid)
>>> from h2o.utils.distributions import CustomDistributionBernoulli
>>> custom_distribution_bernoulli = h2o.upload_custom_distribution(CustomDistributionBernoulli,
...                                                                func_name="custom_bernoulli",
...                                                                func_file="custom_bernoulli.py")
>>> airlines_gbm_custom = H2OGradientBoostingEstimator(ntrees=3,
...                                                    max_depth=5,
...                                                    distribution="custom",
...                                                    custom_distribution_func=custom_distribution_bernoulli,
...                                                    seed=1235)
>>> airlines_gbm_custom.train(x=predictors,
...                           y=response,
...                           training_frame=train,
...                           validation_frame=valid)
>>> airlines_gbm.auc()

custom_metric_func

Reference to custom evaluation function, format: language:keyName=funcName

Type: str.

distribution

Distribution function

One of: "auto", "bernoulli", "quasibinomial", "multinomial", "gaussian", "poisson", "gamma", "tweedie", "laplace", "quantile", "huber", "custom" (default: "auto").

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> response = "cylinders"
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_gbm = H2OGradientBoostingEstimator(distribution="poisson",
...                                         seed=1234)
>>> cars_gbm.train(x=predictors,
...                y=response,
...                training_frame=train,
...                validation_frame=valid)
>>> cars_gbm.mse(valid=True)

export_checkpoints_dir

Automatically export generated models to this directory.

Type: str.

Examples:

>>> airlines = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip", destination_frame="air.hex")
>>> predictors = ["DayofMonth", "DayOfWeek"]
>>> response = "IsDepDelayed"
>>> hyper_parameters = {'ntrees': [5,10]}
>>> search_crit = {'strategy': "RandomDiscrete",
...                'max_models': 5,
...                'seed': 1234,
...                'stopping_rounds': 3,
...                'stopping_metric': "AUTO",
...                'stopping_tolerance': 1e-2}
>>> checkpoints_dir = tempfile.mkdtemp()
>>> air_grid = H2OGridSearch(H2OGradientBoostingEstimator,
...                          hyper_params=hyper_parameters,
...                          search_criteria=search_crit)
>>> air_grid.train(x=predictors,
...                y=response,
...                training_frame=airlines,
...                distribution="bernoulli",
...                learn_rate=0.1,
...                max_depth=3,
...                export_checkpoints_dir=checkpoints_dir)
>>> len(listdir(checkpoints_dir))

fold_assignment

Cross-validation fold assignment scheme, if fold_column is not specified. The ‘Stratified’ option will stratify the folds based on the response variable, for classification problems.

One of: "auto", "random", "modulo", "stratified" (default: "auto").

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> assignment_type = "Random"
>>> cars_gbm = H2OGradientBoostingEstimator(fold_assignment=assignment_type,
...                                         nfolds=5,
...                                         seed=1234)
>>> cars_gbm.train(x=predictors, y=response, training_frame=cars)
>>> cars_gbm.auc(xval=True)

fold_column

Column with cross-validation fold index assignment per observation.

Type: str.

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> fold_numbers = cars.kfold_column(n_folds=5,
...                                  seed=1234)
>>> fold_numbers.set_names(["fold_numbers"])
>>> cars = cars.cbind(fold_numbers)
>>> cars_gbm = H2OGradientBoostingEstimator(seed=1234)
>>> cars_gbm.train(x=predictors,
...                y=response,
...                training_frame=cars,
...                fold_column="fold_numbers")
>>> cars_gbm.auc(xval=True)

histogram_type

What type of histogram to use for finding optimal split points

One of: "auto", "uniform_adaptive", "random", "quantiles_global", "round_robin" (default: "auto").

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> airlines["Year"] = airlines["Year"].asfactor()
>>> airlines["Month"] = airlines["Month"].asfactor()
>>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
>>> airlines["Cancelled"] = airlines["Cancelled"].asfactor()
>>> airlines['FlightNum'] = airlines['FlightNum'].asfactor()
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> response = "IsDepDelayed"
>>> train, valid = airlines.split_frame(ratios=[.8], seed=1234)
>>> airlines_gbm = H2OGradientBoostingEstimator(histogram_type="UniformAdaptive",
...                                             seed=1234)
>>> airlines_gbm.train(x=predictors,
...                    y=response,
...                    training_frame=train,
...                    validation_frame=valid)
>>> airlines_gbm.auc(valid=True)

huber_alpha

Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1).

Type: float (default: 0.9).

Examples:

>>> insurance = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/insurance.csv")
>>> predictors = insurance.columns[0:4]
>>> response = 'Claims'
>>> insurance['Group'] = insurance['Group'].asfactor()
>>> insurance['Age'] = insurance['Age'].asfactor()
>>> train, valid = insurance.split_frame(ratios=[.8], seed=1234)
>>> insurance_gbm = H2OGradientBoostingEstimator(distribution="huber",
...                                              huber_alpha=0.9,
...                                              seed=1234)
>>> insurance_gbm.train(x=predictors,
...                     y=response,
...                     training_frame=train,
...                     validation_frame=valid)
>>> insurance_gbm.mse(valid=True)

ignore_const_cols

Ignore constant columns.

Type: bool (default: True).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> cars["const_1"] = 6
>>> cars["const_2"] = 7
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_gbm = H2OGradientBoostingEstimator(seed=1234,
...                                         ignore_const_cols=True)
>>> cars_gbm.train(x=predictors,
...                y=response,
...                training_frame=train,
...                validation_frame=valid)
>>> cars_gbm.auc(valid=True)

ignored_columns

Names of columns to ignore for training.

Type: List[str].

keep_cross_validation_fold_assignment

Whether to keep the cross-validation fold assignment.

Type: bool (default: False).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> folds = 5
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_gbm = H2OGradientBoostingEstimator(keep_cross_validation_fold_assignment=True,
...                                         nfolds=5,
...                                         seed=1234)
>>> cars_gbm.train(x=predictors,
...                y=response,
...                training_frame=train,
...                validation_frame=valid)
>>> cars_gbm.auc()

keep_cross_validation_models

Whether to keep the cross-validation models.

Type: bool (default: True).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> folds = 5
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_gbm = H2OGradientBoostingEstimator(keep_cross_validation_models=True,
...                                         nfolds=5,
...                                         seed=1234)
>>> cars_gbm.train(x=predictors,
...                y=response,
...                training_frame=train,
...                validation_frame=valid)
>>> cars_gbm.auc()

keep_cross_validation_predictions

Whether to keep the predictions of the cross-validation models.

Type: bool (default: False).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> folds = 5
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_gbm = H2OGradientBoostingEstimator(keep_cross_validation_predictions=True,
...                                         nfolds=5,
...                                         seed=1234)
>>> cars_gbm.train(x=predictors,
...                y=response,
...                training_frame=train,
...                validation_frame=valid)
>>> cars_gbm.auc()

learn_rate

Learning rate (from 0.0 to 1.0)

Type: float (default: 0.1).

Examples:

>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv")
>>> titanic['survived'] = titanic['survived'].asfactor()
>>> predictors = titanic.columns
>>> del predictors[1:3]
>>> response = 'survived'
>>> train, valid = titanic.split_frame(ratios=[.8], seed=1234)
>>> titanic_gbm = H2OGradientBoostingEstimator(ntrees=10000,
...                                            learn_rate=0.01,
...                                            stopping_rounds=5,
...                                            stopping_metric="AUC",
...                                            stopping_tolerance=1e-4,
...                                            seed=1234)
>>> titanic_gbm.train(x=predictors,
...                   y=response,
...                   training_frame=train,
...                   validation_frame=valid)
>>> titanic_gbm.auc(valid=True)

learn_rate_annealing

Scale the learning rate by this factor after each tree (e.g., 0.99 or 0.999)

Type: float (default: 1).

Examples:

>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv")
>>> titanic['survived'] = titanic['survived'].asfactor()
>>> predictors = titanic.columns
>>> del predictors[1:3]
>>> response = 'survived'
>>> train, valid = titanic.split_frame(ratios=[.8], seed=1234)
>>> titanic_gbm = H2OGradientBoostingEstimator(ntrees=10000,
...                                            learn_rate=0.05,
...                                            learn_rate_annealing=.9,
...                                            stopping_rounds=5,
...                                            stopping_metric="AUC",
...                                            stopping_tolerance=1e-4,
...                                            seed=1234)
>>> titanic_gbm.train(x=predictors,
...                   y=response,
...                   training_frame=train,
...                   validation_frame=valid)
>>> titanic_gbm.auc(valid=True)

max_abs_leafnode_pred

Maximum absolute value of a leaf node prediction

Type: float (default: 1.797693135e+308).

Examples:

>>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data")
>>> covtype[54] = covtype[54].asfactor()
>>> predictors = covtype.columns[0:54]
>>> response = 'C55'
>>> train, valid = covtype.split_frame(ratios=[.8], seed=1234)
>>> cov_gbm = H2OGradientBoostingEstimator(max_abs_leafnode_pred=2,
...                                        seed=1234)
>>> cov_gbm.train(x=predictors,
...               y=response,
...               training_frame=train,
...               validation_frame=valid)
>>> cov_gbm.logloss(valid=True)

max_after_balance_size

Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires balance_classes.

Type: float (default: 5).

Examples:

>>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data")
>>> covtype[54] = covtype[54].asfactor()
>>> predictors = covtype.columns[0:54]
>>> response = 'C55'
>>> train, valid = covtype.split_frame(ratios=[.8], seed=1234)
>>> max = .85
>>> cov_gbm = H2OGradientBoostingEstimator(balance_classes=True,
...                                        max_after_balance_size=max,
...                                        seed=1234)
>>> cov_gbm.train(x=predictors,
...               y=response,
...               training_frame=train,
...               validation_frame=valid)
>>> cov_gbm.logloss(valid=True)

max_confusion_matrix_size

[Deprecated] Maximum size (# classes) for confusion matrices to be printed in the Logs

Type: int (default: 20).

max_depth

Maximum tree depth.

Type: int (default: 5).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_gbm = H2OGradientBoostingEstimator(ntrees=100,
...                                         max_depth=2,
...                                         seed=1234)
>>> cars_gbm.train(x=predictors,
...                y=response,
...                training_frame=train,
...                validation_frame=valid)
>>> cars_gbm.auc(valid=True)

max_hit_ratio_k

Max. number (top K) of predictions to use for hit ratio computation (for multi-class only, 0 to disable)

Type: int (default: 0).

Examples:

>>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data")
>>> covtype[54] = covtype[54].asfactor()
>>> predictors = covtype.columns[0:54]
>>> response = 'C55'
>>> train, valid = covtype.split_frame(ratios=[.8], seed=1234)
>>> cov_gbm = H2OGradientBoostingEstimator(max_hit_ratio_k=3,
...                                        seed=1234)
>>> cov_gbm.train(x=predictors,
...               y=response,
...               training_frame=train,
...               validation_frame=valid)
>>> cov_gbm.logloss(valid=True)

max_runtime_secs

Maximum allowed runtime in seconds for model training. Use 0 to disable.

Type: float (default: 0).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_gbm = H2OGradientBoostingEstimator(max_runtime_secs=10,
...                                         ntrees=10000,
...                                         max_depth=10,
...                                         seed=1234)
>>> cars_gbm.train(x=predictors,
...                y=response,
...                training_frame=train,
...                validation_frame=valid)
>>> cars_gbm.auc(valid=True)

min_rows

Fewest allowed (weighted) observations in a leaf.

Type: float (default: 10).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_gbm = H2OGradientBoostingEstimator(min_rows=16,
...                                         seed=1234)
>>> cars_gbm.train(x=predictors,
...                y=response,
...                training_frame=train,
...                validation_frame=valid)
>>> cars_gbm.auc(valid=True)

min_split_improvement

Minimum relative improvement in squared error reduction for a split to happen

Type: float (default: 1e-05).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_gbm = H2OGradientBoostingEstimator(min_split_improvement=1e-3,
...                                         seed=1234)
>>> cars_gbm.train(x=predictors,
...                y=response,
...                training_frame=train,
...                validation_frame=valid)
>>> cars_gbm.auc(valid=True)

monotone_constraints

A mapping representing monotonic constraints. Use +1 to enforce an increasing constraint and -1 to specify a decreasing constraint.

Type: dict.

Examples:

>>> prostate_hex = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv.zip")
>>> prostate_hex["CAPSULE"] = prostate_hex["CAPSULE"].asfactor()
>>> response = "CAPSULE"
>>> seed = 42
>>> monotone_constraints = {"AGE":1}
>>> gbm_model = H2OGradientBoostingEstimator(seed=seed,
...                                          monotone_constraints=monotone_constraints)
>>> gbm_model.train(y=response,
...                 ignored_columns=["ID"],
...                 training_frame=prostate_hex)
>>> gbm_model.scoring_history()

nbins

For numerical columns (real/int), build a histogram of (at least) this many bins, then split at the best point

Type: int (default: 20).

Examples:

>>> eeg = h2o.import_file("https://h2o-public-test-data.s3.amazonaws.com/smalldata/eeg/eeg_eyestate.csv")
>>> eeg['eyeDetection'] = eeg['eyeDetection'].asfactor()
>>> predictors = eeg.columns[:-1]
>>> response = 'eyeDetection'
>>> train, valid = eeg.split_frame(ratios=[.8], seed=1234)
>>> bin_num = [16, 32, 64, 128, 256, 512]
>>> label = ["16", "32", "64", "128", "256", "512"]
>>> for key, num in enumerate(bin_num):
...     eeg_gbm = H2OGradientBoostingEstimator(nbins=num, seed=1234)
...     eeg_gbm.train(x=predictors,
...                   y=response,
...                   training_frame=train,
...                   validation_frame=valid)
...     print(label[key], 'training score', eeg_gbm.auc(train=True)) 
...     print(label[key], 'validation score', eeg_gbm.auc(valid=True))

nbins_cats

For categorical columns (factors), build a histogram of this many bins, then split at the best point. Higher values can lead to more overfitting.

Type: int (default: 1024).

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> airlines["Year"] = airlines["Year"].asfactor()
>>> airlines["Month"] = airlines["Month"].asfactor()
>>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
>>> airlines["Cancelled"] = airlines["Cancelled"].asfactor()
>>> airlines['FlightNum'] = airlines['FlightNum'].asfactor()
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> response = "IsDepDelayed"
>>> train, valid = airlines.split_frame(ratios=[.8], seed=1234)
>>> bin_num = [8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096]
>>> label = ["8", "16", "32", "64", "128", "256", "512", "1024", "2048", "4096"]
>>> for key, num in enumerate(bin_num):
...     airlines_gbm = H2OGradientBoostingEstimator(nbins_cats=num, seed=1234)
...     airlines_gbm.train(x=predictors,
...                        y=response,
...                        training_frame=train,
...                        validation_frame=valid)
...     print(label[key], 'training score', airlines_gbm.auc(train=True))
...     print(label[key], 'validation score', airlines_gbm.auc(valid=True))

nbins_top_level

For numerical columns (real/int), build a histogram of (at most) this many bins at the root level, then decrease by factor of two per level

Type: int (default: 1024).

Examples:

>>> eeg = h2o.import_file("https://h2o-public-test-data.s3.amazonaws.com/smalldata/eeg/eeg_eyestate.csv")
>>> eeg['eyeDetection'] = eeg['eyeDetection'].asfactor()
>>> predictors = eeg.columns[:-1]
>>> response = 'eyeDetection'
>>> train, valid = eeg.split_frame(ratios=[.8], seed=1234)
>>> bin_num = [32, 64, 128, 256, 512, 1024, 2048, 4096]
>>> label = ["32", "64", "128", "256", "512", "1024", "2048", "4096"]
>>> for key, num in enumerate(bin_num):
...     eeg_gbm = H2OGradientBoostingEstimator(nbins_top_level=num, seed=1234)
...     eeg_gbm.train(x=predictors,
...                   y=response,
...                   training_frame=train,
...                   validation_frame=valid)
...     print(label[key], 'training score', eeg_gbm.auc(train=True)) 
...     print(label[key], 'validation score', eeg_gbm.auc(valid=True))

nfolds

Number of folds for K-fold cross-validation (0 to disable or >= 2).

Type: int (default: 0).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> folds = 5
>>> cars_gbm = H2OGradientBoostingEstimator(nfolds=folds,
...                                         seed=1234
>>> cars_gbm.train(x=predictors,
...                y=response,
...                training_frame=cars)
>>> cars_gbm.auc()

ntrees

Number of trees.

Type: int (default: 50).

Examples:

>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv")
>>> titanic['survived'] = titanic['survived'].asfactor()
>>> predictors = titanic.columns
>>> del predictors[1:3]
>>> response = 'survived'
>>> train, valid = titanic.split_frame(ratios=[.8], seed=1234)
>>> tree_num = [20, 50, 80, 110, 140, 170, 200]
>>> label = ["20", "50", "80", "110", "140", "170", "200"]
>>> for key, num in enumerate(tree_num):
...     titanic_gbm = H2OGradientBoostingEstimator(ntrees=num,
...                                                seed=1234)
...     titanic_gbm.train(x=predictors,
...                       y=response,
...                       training_frame=train,
...                       validation_frame=valid)
...     print(label[key], 'training score', titanic_gbm.auc(train=True))
...     print(label[key], 'validation score', titanic_gbm.auc(valid=True))

offset_column

Offset column. This will be added to the combination of columns before applying the link function.

Type: str.

Examples:

>>> boston = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv")
>>> predictors = boston.columns[:-1]
>>> response = "medv"
>>> boston['chas'] = boston['chas'].asfactor()
>>> boston["offset"] = boston["medv"].log()
>>> train, valid = boston.split_frame(ratios=[.8], seed=1234)
>>> boston_gbm = H2OGradientBoostingEstimator(offset_column="offset",
...                                           seed=1234)
>>> boston_gbm.train(x=predictors,
...                  y=response,
...                  training_frame=train,
...                  validation_frame=valid)
>>> boston_gbm.mse(valid=True)

pred_noise_bandwidth

Bandwidth (sigma) of Gaussian multiplicative noise ~N(1,sigma) for tree node predictions

Type: float (default: 0).

Examples:

>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv")
>>> titanic['survived'] = titanic['survived'].asfactor()
>>> predictors = titanic.columns
>>> del predictors[1:3]
>>> response = 'survived'
>>> train, valid = titanic.split_frame(ratios=[.8], seed=1234)
>>> titanic_gbm = H2OGradientBoostingEstimator(pred_noise_bandwidth=0.1,
...                                            seed=1234)
>>> titanic_gbm.train(x=predictors,
...                   y=response,
...                   training_frame=train,
...                   validation_frame=valid)
>>> titanic_gbm.auc(valid = True)

quantile_alpha

Desired quantile for Quantile regression, must be between 0 and 1.

Type: float (default: 0.5).

Examples:

>>> boston = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv")
>>> predictors = boston.columns[:-1]
>>> response = "medv"
>>> boston['chas'] = boston['chas'].asfactor()
>>> train, valid = boston.split_frame(ratios=[.8], seed=1234)
>>> boston_gbm = H2OGradientBoostingEstimator(distribution="quantile",
...                                           quantile_alpha=.8,
...                                           seed=1234)
>>> boston_gbm.train(x=predictors,
...                  y=response,
...                  training_frame=train,
...                  validation_frame=valid)
>>> boston_gbm.mse(valid=True)

r2_stopping

r2_stopping is no longer supported and will be ignored if set - please use stopping_rounds, stopping_metric and stopping_tolerance instead. Previous version of H2O would stop making trees when the R^2 metric equals or exceeds this

Type: float (default: 1.797693135e+308).

response_column

Response variable column.

Type: str.

sample_rate

Row sample rate per tree (from 0.0 to 1.0)

Type: float (default: 1).

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> airlines["Month"] = airlines["Month"].asfactor()                             >>> airlines["Year"]= airlines["Year"].asfactor()
>>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
>>> airlines["Cancelled"] = airlines["Cancelled"].asfactor()
>>> airlines['FlightNum'] = airlines['FlightNum'].asfactor()
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> response = "IsDepDelayed"
>>> train, valid = airlines.split_frame(ratios=[.8], seed=1234)
>>> airlines_gbm = H2OGradientBoostingEstimator(sample_rate=.7,
...                                             seed=1234)
>>> airlines_gbm.train(x=predictors,
...                    y=response,
...                    training_frame=train,
...                    validation_frame=valid)
>>> airlines_gbm.auc(valid=True)

sample_rate_per_class

A list of row sample rates per class (relative fraction for each class, from 0.0 to 1.0), for each tree

Type: List[float].

Examples:

>>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data")
>>> covtype[54] = covtype[54].asfactor()
>>> predictors = covtype.columns[0:54]
>>> response = 'C55'
>>> train, valid = covtype.split_frame(ratios=[.8], seed=1234)
>>> rate_per_class_list = [1, .4, 1, 1, 1, 1, 1]
>>> cov_gbm = H2OGradientBoostingEstimator(sample_rate_per_class=rate_per_class_list,
...                                        seed=1234)
>>> cov_gbm.train(x=predictors,
...               y=response,
...               training_frame=train,
...               validation_frame=valid)
>>> cov_gbm.logloss(valid=True)

score_each_iteration

Whether to score during each iteration of model training.

Type: bool (default: False).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> train, valid = cars.split_frame(ratios=[.8],
...                                 seed=1234)
>>> cars_gbm = H2OGradientBoostingEstimator(score_each_iteration=True,
...                                         ntrees=55,
...                                         seed=1234)
>>> cars_gbm.train(x=predictors,
...                y=response,
...                training_frame=train,
...                validation_frame=valid)
>>> cars_gbm.scoring_history()

score_tree_interval

Score the model after every so many trees. Disabled if set to 0.

Type: int (default: 0).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> train, valid = cars.split_frame(ratios=[.8],
...                                 seed=1234)
>>> cars_gbm = H2OGradientBoostingEstimator(score_tree_interval=True,
...                                         ntrees=55,
...                                         seed=1234)
>>> cars_gbm.train(x=predictors,
...                y=response,
...                training_frame=train,
...                validation_frame=valid)
>>> cars_gbm.scoring_history()

seed

Seed for pseudo random number generator (if applicable)

Type: int (default: -1).

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> airlines["Year"] = airlines["Year"].asfactor()
>>> airlines["Month"] = airlines["Month"].asfactor()
>>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
>>> airlines["Cancelled"] = airlines["Cancelled"].asfactor()
>>> airlines['FlightNum'] = airlines['FlightNum'].asfactor()
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> response = "IsDepDelayed"
>>> train, valid = airlines.split_frame(ratios=[.8], seed=1234)
>>> gbm_w_seed_1 = H2OGradientBoostingEstimator(col_sample_rate=.7,
...                                             seed=1234)
>>> gbm_w_seed_1.train(x=predictors,
...                    y=response,
...                    training_frame=train,
...                    validation_frame=valid)
>>> print('auc for the 1st model built with a seed:', gbm_w_seed_1.auc(valid=True))

stopping_metric

Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anonomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client.

One of: "auto", "deviance", "logloss", "mse", "rmse", "mae", "rmsle", "auc", "aucpr", "lift_top_group", "misclassification", "mean_per_class_error", "custom", "custom_increasing" (default: "auto").

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> airlines["Year"] = airlines["Year"].asfactor()
>>> airlines["Month"] = airlines["Month"].asfactor()
>>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
>>> airlines["Cancelled"] = airlines["Cancelled"].asfactor()
>>> airlines['FlightNum'] = airlines['FlightNum'].asfactor()
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> response = "IsDepDelayed"
>>> train, valid = airlines.split_frame(ratios=[.8], seed=1234)
>>> airlines_gbm = H2OGradientBoostingEstimator(stopping_metric="auc",
...                                             stopping_rounds=3,
...                                             stopping_tolerance=1e-2,
...                                             seed=1234)
>>> airlines_gbm.train(x=predictors,
...                    y=response,
...                    training_frame=train,
...                    validation_frame=valid)
>>> airlines_gbm.auc(valid=True)

stopping_rounds

Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable)

Type: int (default: 0).

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> airlines["Year"] = airlines["Year"].asfactor()
>>> airlines["Month"] = airlines["Month"].asfactor()
>>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
>>> airlines["Cancelled"] = airlines["Cancelled"].asfactor()
>>> airlines['FlightNum'] = airlines['FlightNum'].asfactor()
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> response = "IsDepDelayed"
>>> train, valid = airlines.split_frame(ratios=[.8], seed=1234)
>>> airlines_gbm = H2OGradientBoostingEstimator(stopping_metric="auc",
...                                             stopping_rounds=3,
...                                             stopping_tolerance=1e-2,
...                                             seed=1234)
>>> airlines_gbm.train(x=predictors,
...                    y=response,
...                    training_frame=train,
...                    validation_frame=valid)
>>> airlines_gbm.auc(valid=True)

stopping_tolerance

Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much)

Type: float (default: 0.001).

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> airlines["Year"] = airlines["Year"].asfactor()
>>> airlines["Month"] = airlines["Month"].asfactor()
>>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
>>> airlines["Cancelled"] = airlines["Cancelled"].asfactor()
>>> airlines['FlightNum'] = airlines['FlightNum'].asfactor()
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> response = "IsDepDelayed"
>>> train, valid= airlines.split_frame(ratios=[.8], seed=1234)
>>> airlines_gbm = H2OGradientBoostingEstimator(stopping_metric="auc",
...                                             stopping_rounds=3,
...                                             stopping_tolerance=1e-2,
...                                             seed=1234)
>>> airlines_gbm.train(x=predictors,
...                    y=response,
...                    training_frame=train,
...                    validation_frame=valid)
>>> airlines_gbm.auc(valid=True)

training_frame

Id of the training data frame.

Type: H2OFrame.

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_gbm = H2OGradientBoostingEstimator(seed=1234)
>>> cars_gbm.train(x=predictors,
...                y=response,
...                training_frame=train,
...                validation_frame=valid)
>>> cars_gbm.auc(valid=True)

tweedie_power

Tweedie power for Tweedie regression, must be between 1 and 2.

Type: float (default: 1.5).

Examples:

>>> insurance = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/insurance.csv")
>>> predictors = insurance.columns[0:4]
>>> response = 'Claims'
>>> insurance['Group'] = insurance['Group'].asfactor()
>>> insurance['Age'] = insurance['Age'].asfactor()
>>> train, valid = insurance.split_frame(ratios=[.8], seed=1234)
>>> insurance_gbm = H2OGradientBoostingEstimator(distribution="tweedie",
...                                              tweedie_power=1.2,
...                                              seed=1234)
>>> insurance_gbm.train(x=predictors,
...                     y=response,
...                     training_frame=train,
...                     validation_frame=valid)
>>> insurance_gbm.mse(valid=True)

validation_frame

Id of the validation data frame.

Type: H2OFrame.

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_gbm = H2OGradientBoostingEstimator(seed=1234)
>>> cars_gbm.train(x=predictors,
...                y=response,
...                training_frame=train,
...                validation_frame=valid)
>>> cars_gbm.auc(valid=True)

weights_column

Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor.

Type: str.

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_gbm = H2OGradientBoostingEstimator(seed=1234)
>>> cars_gbm.train(x=predictors,
...                y=response,
...                training_frame=train,
...                validation_frame=valid,
...                weights_column="weight")
>>> cars_gbm.auc(valid=True)

H2OGeneralizedLinearEstimator

class h2o.estimators.glm.H2OGeneralizedLinearEstimator(**kwargs)

Bases: h2o.estimators.estimator_base.H2OEstimator

Generalized Linear Modeling

Fits a generalized linear model, specified by a response variable, a set of predictors, and a description of the error distribution.

A subclass of ModelBase is returned. The specific subclass depends on the machine learning task at hand (if it’s binomial classification, then an H2OBinomialModel is returned, if it’s regression then a H2ORegressionModel is returned). The default print-out of the models is shown, but further GLM-specific information can be queried out of the object. Upon completion of the GLM, the resulting object has coefficients, normalized coefficients, residual/null deviance, aic, and a host of model metrics including MSE, AUC (for logistic regression), degrees of freedom, and confusion matrices.

HGLM

If set to true, will return HGLM model. Otherwise, normal GLM model will be returned

Type: bool (default: False).

Lambda

DEPRECATED. Use self.lambda_ instead

alpha

Distribution of regularization between the L1 (Lasso) and L2 (Ridge) penalties. A value of 1 for alpha represents Lasso regression, a value of 0 produces Ridge regression, and anything in between specifies the amount of mixing between the two. Default value of alpha is 0 when SOLVER = ‘L-BFGS’; 0.5 otherwise.

Type: List[float].

Examples:

>>> boston = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv")
>>> predictors = boston.columns[:-1]
>>> response = "medv"
>>> boston['chas'] = boston['chas'].asfactor()
>>> train, valid = boston.split_frame(ratios=[.8])
>>> boston_glm = H2OGeneralizedLinearEstimator(alpha=.25)
>>> boston_glm.train(x=predictors,
...                  y=response,
...                  training_frame=train,
...                  validation_frame=valid)
>>> print(boston_glm.mse(valid=True))

balance_classes

Balance training data class counts via over/under-sampling (for imbalanced data).

Type: bool (default: False).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> predictors = ["displacement","power","weight","year"]
>>> response = "acceleration"
>>> train, valid = cars.split_frame(ratios=[.8])
>>> cars_glm = H2OGeneralizedLinearEstimator(balance_classes=True,
...                                          seed=1234)
>>> cars_glm.train(x=predictors,
...                y=response,
...                training_frame=train,
...                validation_frame=valid)
>>> cars_glm.mse()

beta_constraints

Beta constraints

Type: H2OFrame.

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> predictors = ["displacement","power","weight","year"]
>>> response = "acceleration"
>>> train, valid = cars.split_frame(ratios=[.8])
>>> n = len(predictors)
>>> constraints = h2o.H2OFrame({'names':predictors,
...                             'lower_bounds': [-1000]*n,
...                             'upper_bounds': [1000]*n,
...                             'beta_given': [1]*n,
...                             'rho': [0.2]*n})
>>> cars_glm = H2OGeneralizedLinearEstimator(standardize=True,
...                                          beta_constraints=constraints)
>>> cars_glm.train(x=predictors,
...                y=response,
...                training_frame=train,
...                validation_frame=valid)
>>> cars_glm.mse()

beta_epsilon

Converge if beta changes less (using L-infinity norm) than beta esilon, ONLY applies to IRLSM solver

Type: float (default: 0.0001).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> predictors = ["displacement","power","weight","year"]
>>> response = "acceleration"
>>> train, valid = cars.split_frame(ratios=[.8])
>>> cars_glm = H2OGeneralizedLinearEstimator(beta_epsilon=1e-3)
>>> cars_glm.train(x=predictors,
...                y=response,
...                training_frame=train,
...                validation_frame=valid)
>>> cars_glm.mse()

calc_like

if true, will return likelihood function value for HGLM.

Type: bool (default: False).

class_sampling_factors

Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will be automatically computed to obtain class balance during training. Requires balance_classes.

Type: List[float].

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> predictors = ["displacement","power","weight","year"]
>>> response = "acceleration"
>>> train, valid = cars.split_frame(ratios=[.8])
>>> sample_factors = [1., 0.5, 1., 1., 1., 1., 1.]
>>> cars_glm = H2OGeneralizedLinearEstimator(balance_classes=True,
...                                          class_sampling_factors=sample_factors,
...                                          seed=1234)
>>> cars_glm.train(x=predictors,
...                y=response,
...                training_frame=train,
...                validation_frame=valid)
>>> cars_glm.mse()

compute_p_values

Request p-values computation, p-values work only with IRLSM solver and no regularization

Type: bool (default: False).

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> airlines["Year"] = airlines["Year"].asfactor()
>>> airlines["Month"] = airlines["Month"].asfactor()
>>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
>>> airlines["Cancelled"] = airlines["Cancelled"].asfactor()
>>> airlines['FlightNum'] = airlines['FlightNum'].asfactor()
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> response = "IsDepDelayed"
>>> train, valid= airlines.split_frame(ratios=[.8])
>>> airlines_glm = H2OGeneralizedLinearEstimator(family='binomial',
...                                              lambda_=0,
...                                              remove_collinear_columns=True,
...                                              compute_p_values=True)
>>> airlines_glm.train(x=predictors,
...                    y=response,
...                    training_frame=train,
...                    validation_frame=valid)
>>> airlines_glm.mse()

custom_metric_func

Reference to custom evaluation function, format: language:keyName=funcName

Type: str.

early_stopping

Stop early when there is no more relative improvement on train or validation (if provided)

Type: bool (default: True).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> train, valid = cars.split_frame(ratios=[.8])
>>> cars_glm = H2OGeneralizedLinearEstimator(family='binomial',
...                                          early_stopping=True)
>>> cars_glm.train(x=predictors,
...                y=response,
...                training_frame=train,
...                validation_frame=valid)
>>> cars_glm.auc(valid=True)

export_checkpoints_dir

Automatically export generated models to this directory.

Type: str.

Examples:

>>> import tempfile
>>> from os import listdir
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> predictors = ["displacement","power","weight","year"]
>>> response = "acceleration"
>>> train, valid = cars.split_frame(ratios=[.8])
>>> checkpoints = tempfile.mkdtemp()
>>> cars_glm = H2OGeneralizedLinearEstimator(export_checkpoints_dir=checkpoints,
...                                          seed=1234)
>>> cars_glm.train(x=predictors,
...                y=response,
...                training_frame=train,
...                validation_frame=valid)
>>> cars_glm.mse()
>>> len(listdir(checkpoints_dir))

family

Family. Use binomial for classification with logistic regression, others are for regression problems.

One of: "gaussian", "binomial", "quasibinomial", "ordinal", "multinomial", "poisson", "gamma", "tweedie", "negativebinomial" (default: "gaussian").

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> train, valid = cars.split_frame(ratios=[.8])
>>> cars_glm = H2OGeneralizedLinearEstimator(family='binomial')
>>> cars_glm.train(x=predictors,
...                y=response,
...                training_frame=train,
...                validation_frame=valid)
>>> cars_glm.auc(valid = True)

fold_assignment

Cross-validation fold assignment scheme, if fold_column is not specified. The ‘Stratified’ option will stratify the folds based on the response variable, for classification problems.

One of: "auto", "random", "modulo", "stratified" (default: "auto").

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> assignment_type = "Random"
>>> cars_gml = H2OGeneralizedLinearEstimator(fold_assignment=assignment_type,
...                                          nfolds=5,
...                                          family='binomial',
...                                          seed=1234)
>>> cars_glm.train(x=predictors,
...                y=response,
...                training_frame=cars)
>>> cars_glm.auc(train=True)

fold_column

Column with cross-validation fold index assignment per observation.

Type: str.

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> fold_numbers = cars.kfold_column(n_folds=5, seed=1234)
>>> fold_numbers.set_names(["fold_numbers"])
>>> cars = cars.cbind(fold_numbers)
>>> print(cars['fold_numbers'])
>>>  cars_glm = H2OGeneralizedLinearEstimator(seed=1234,
...                                           family="binomial")
>>> cars_glm.train(x=predictors,
...                y=response,
...                training_frame=cars,
...                fold_column="fold_numbers")
>>> cars_glm.auc(xval=True)

static getGLMRegularizationPath(model)

Extract full regularization path explored during lambda search from glm model.

Parameters:model – source lambda search model Examples:

>>> d = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv")
>>> m = H2OGeneralizedLinearEstimator(family = 'binomial',
...                                   lambda_search = True,
...                                   solver = 'COORDINATE_DESCENT')
>>> m.train(training_frame = d,
...         x = [2,3,4,5,6,7,8],
...         y = 1)
>>> r = H2OGeneralizedLinearEstimator.getGLMRegularizationPath(m)
>>> m2 = H2OGeneralizedLinearEstimator.makeGLMModel(model=m,
...                                                 coefs=r['coefficients'][10])
>>> dev1 = r['explained_deviance_train'][10]
>>> p = m2.model_performance(d)
>>> dev2 = 1-p.residual_deviance()/p.null_deviance()
>>> print(dev1, " =?= ", dev2)

gradient_epsilon

Converge if objective changes less (using L-infinity norm) than this, ONLY applies to L-BFGS solver. Default indicates: If lambda_search is set to False and lambda is equal to zero, the default value of gradient_epsilon is equal to .000001, otherwise the default value is .0001. If lambda_search is set to True, the conditional values above are 1E-8 and 1E-6 respectively.

Type: float (default: -1).

Examples:

>>> boston = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv")
>>> predictors = boston.columns[:-1]
>>> response = "medv"
>>> boston['chas'] = boston['chas'].asfactor()
>>> train, valid = boston.split_frame(ratios=[.8])
>>> boston_glm = H2OGeneralizedLinearEstimator(gradient_epsilon=1e-3)
>>> boston_glm.train(x=predictors,
...                  y=response,
...                  training_frame=train,
...                  validation_frame=valid)
>>> boston_glm.mse()

ignore_const_cols

Ignore constant columns.

Type: bool (default: True).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> cars["const_1"] = 6
>>> cars["const_2"] = 7
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_glm = H2OGeneralizedLinearEstimator(seed=1234,
...                                          ignore_const_cols=True,
...                                          family="binomial")
>>> cars_glm.train(x=predictors,
...                y=response,
...                training_frame=train,
...                validation_frame=valid)
>>> cars_glm.auc(valid=True)

ignored_columns

Names of columns to ignore for training.

Type: List[str].

interaction_pairs

A list of pairwise (first order) column interactions.

Type: List[tuple].

Examples:

>>> df = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> XY = [df.names[i-1] for i in [1,2,3,4,6,8,9,13,17,18,19,31]]
>>> interactions = [XY[i-1] for i in [5,7,9]]
>>> m = H2OGeneralizedLinearEstimator(lambda_search=True,
...                                   family="binomial",
...                                   interactions=interactions)
>>> m.train(x=XY[:len(XY)], y=XY[-1],training_frame=df)
>>> m._model_json['output']['coefficients_table']
>>> coef_m = m._model_json['output']['coefficients_table']
>>> interaction_pairs = [("CRSDepTime", "UniqueCarrier"),
...                      ("CRSDepTime", "Origin"),
...                      ("UniqueCarrier", "Origin")]
>>> mexp = H2OGeneralizedLinearEstimator(lambda_search=True,
...                                      family="binomial",
...                                      interaction_pairs=interaction_pairs)
>>> mexp.train(x=XY[:len(XY)], y=XY[-1],training_frame=df)
>>> mexp._model_json['output']['coefficients_table']

interactions

A list of predictor column indices to interact. All pairwise combinations will be computed for the list.

Type: List[str].

Examples:

>>> boston = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv")
>>> predictors = boston.columns[:-1]
>>> response = "medv"
>>> boston['chas'] = boston['chas'].asfactor()
>>> train, valid = boston.split_frame(ratios=[.8])
>>> interactions_list = ['crim', 'dis']
>>> boston_glm = H2OGeneralizedLinearEstimator(interactions=interactions_list) 
>>> boston_glm.train(x=predictors,
...                  y=response,
...                  training_frame=train,
...                  validation_frame=valid)
>>> boston_glm.mse()

intercept

Include constant term in the model

Type: bool (default: True).

Examples:

>>> iris = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_wheader.csv")
>>> iris['class'] = iris['class'].asfactor()
>>> predictors = iris.columns[:-1]
>>> response = 'class'
>>> train, valid = iris.split_frame(ratios=[.8])
>>> iris_glm = H2OGeneralizedLinearEstimator(family='multinomial',
...                                          intercept=True)
>>> iris_glm.train(x=predictors,
...                y=response,
...                training_frame=train,
...                validation_frame=valid)
>>> iris_glm.logloss(valid=True)

keep_cross_validation_fold_assignment

Whether to keep the cross-validation fold assignment.

Type: bool (default: False).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_glm = H2OGeneralizedLinearEstimator(keep_cross_validation_fold_assignment=True,
...                                          nfolds=5,
...                                          seed=1234,
...                                          family="binomial")
>>> cars_glm.train(x=predictors,
...                y=response,
...                training_frame=train)
>>> cars_glm.cross_validation_fold_assignment()

keep_cross_validation_models

Whether to keep the cross-validation models.

Type: bool (default: True).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_glm = H2OGeneralizedLinearEstimator(keep_cross_validation_models=True,
...                                          nfolds=5,
...                                          seed=1234,
...                                          family="binomial")
>>> cars_glm.train(x=predictors,
...                y=response,
...                training_frame=train)
>>> cars_glm_cv_models = cars_glm.cross_validation_models()
>>> print(cars_glm.cross_validation_models())

keep_cross_validation_predictions

Whether to keep the predictions of the cross-validation models.

Type: bool (default: False).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_glm = H2OGeneralizedLinearEstimator(keep_cross_validation_predictions=True,
...                                          nfolds=5,
...                                          seed=1234,
...                                          family="binomial")
>>> cars_glm.train(x=predictors,
...                y=response,
...                training_frame=train)
>>> cars_glm.cross_validation_predictions()

lambda_

Regularization strength

Type: List[float].

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> airlines["Year"] = airlines["Year"].asfactor()
>>> airlines["Month"] = airlines["Month"].asfactor()
>>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
>>> airlines["Cancelled"] = airlines["Cancelled"].asfactor()
>>> airlines['FlightNum'] = airlines['FlightNum'].asfactor()
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> response = "IsDepDelayed"
>>> train, valid = airlines.split_frame(ratios=[.8])
>>> airlines_glm = H2OGeneralizedLinearEstimator(family='binomial',
...                                              lambda_=.0001)
>>> airlines_glm.train(x=predictors,
...                    y=response
...                    trainig_frame=train,
...                    validation_frame=valid)
>>> print(airlines_glm.auc(valid=True))

lambda_min_ratio

Minimum lambda used in lambda search, specified as a ratio of lambda_max (the smallest lambda that drives all coefficients to zero). Default indicates: if the number of observations is greater than the number of variables, then lambda_min_ratio is set to 0.0001; if the number of observations is less than the number of variables, then lambda_min_ratio is set to 0.01.

Type: float (default: -1).

Examples:

>>> boston = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv")
>>> predictors = boston.columns[:-1]
>>> response = "medv"
>>> boston['chas'] = boston['chas'].asfactor()
>>> train, valid = boston.split_frame(ratios=[.8])
>>> boston_glm = H2OGeneralizedLinearEstimator(lambda_min_ratio=.0001)
>>> boston_glm.train(x=predictors,
...                  y=response,
...                  training_frame=train,
...                  validation_frame=valid)
>>> boston_glm.mse()

lambda_search

Use lambda search starting at lambda max, given lambda is then interpreted as lambda min

Type: bool (default: False).

Examples:

>>> boston = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv")
>>> predictors = boston.columns[:-1]
>>> response = "medv"
>>> boston['chas'] = boston['chas'].asfactor()
>>> train, valid = boston.split_frame(ratios=[.8])
>>> boston_glm = H2OGeneralizedLinearEstimator(lambda_search=True)
>>> boston_glm.train(x=predictors,
...                  y=response,
...                  training_frame=train,
...                  validation_frame=valid)
>>> print(boston_glm.mse(valid=True))

link

Link function.

One of: "family_default", "identity", "logit", "log", "inverse", "tweedie", "ologit" (default: "family_default").

Examples:

>>> iris = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_wheader.csv")
>>> iris['class'] = iris['class'].asfactor()
>>> predictors = iris.columns[:-1]
>>> response = 'class'
>>> train, valid = iris.split_frame(ratios=[.8])
>>> iris_glm = H2OGeneralizedLinearEstimator(family='multinomial',
...                                          link='family_default')
>>> iris_glm.train(x=predictors,
...                y=response,
...                training_frame=train,
...                validation_frame=valid)
>>> iris_glm.logloss()

static makeGLMModel(model, coefs, threshold=0.5)

Create a custom GLM model using the given coefficients.

Needs to be passed source model trained on the dataset to extract the dataset information from.

Parameters:

• model – source model, used for extracting dataset information
• coefs – dictionary containing model coefficients
• threshold – (optional, only for binomial) decision threshold used for classification

Examples:

>>> d = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv")
>>> m = H2OGeneralizedLinearEstimator(family='binomial',
...                                   lambda_search=True,
...                                   solver='COORDINATE_DESCENT')
>>> m.train(training_frame=d,
...         x=[2,3,4,5,6,7,8],
...         y=1)
>>> r = H2OGeneralizedLinearEstimator.getGLMRegularizationPath(m)
>>> m2 = H2OGeneralizedLinearEstimator.makeGLMModel(model=m,
...                                                 coefs=r['coefficients'][10])
>>> dev1 = r['explained_deviance_train'][10]
>>> p = m2.model_performance(d)
>>> dev2 = 1-p.residual_deviance()/p.null_deviance()
>>> print(dev1, " =?= ", dev2)

max_active_predictors

Maximum number of active predictors during computation. Use as a stopping criterion to prevent expensive model building with many predictors. Default indicates: If the IRLSM solver is used, the value of max_active_predictors is set to 5000 otherwise it is set to 100000000.

Type: int (default: -1).

Examples:

>>> higgs= h2o.import_file("https://h2o-public-test-data.s3.amazonaws.com/smalldata/testng/higgs_train_5k.csv")
>>> predictors = higgs.names
>>> predictors.remove('response')
>>> response = "response"
>>> train, valid = higgs.split_frame(ratios=[.8])
>>> higgs_glm = H2OGeneralizedLinearEstimator(family='binomial',
...                                           max_active_predictors=200)
>>> higgs_glm.train(x=predictors,
...                 y=response,
...                 training_frame=train,
...                 validation_frame=valid)
>>> higgs_glm.auc()

max_after_balance_size

Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires balance_classes.

Type: float (default: 5).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> predictors = ["displacement","power","weight","year"]
>>> response = "acceleration"
>>> train, valid = cars.split_frame(ratios=[.8])
>>> max = .85
>>> cars_glm = H2OGeneralizedLinearEstimator(balance_classes=True,
...                                          max_after_balance_size=max,
...                                          seed=1234)
>>> cars_glm.train(x=predictors,
...                y=response,
...                training_frame=train,
...                validation_frame=valid)
>>> cars_glm.mse()

max_confusion_matrix_size

[Deprecated] Maximum size (# classes) for confusion matrices to be printed in the Logs

Type: int (default: 20).

max_hit_ratio_k

Maximum number (top K) of predictions to use for hit ratio computation (for multi-class only, 0 to disable)

Type: int (default: 0).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> predictors = ["displacement","power","weight","year"]
>>> response = "acceleration"
>>> train, valid = cars.split_frame(ratios=[.8])
>>> cars_glm = H2OGeneralizedLinearEstimator(max_hit_ratio_k=3,
...                                          seed=1234)
>>> cars_glm.train(x=predictors,
...                y=response,
...                training_frame=train,
...                validation_frame=valid)
>>> cars_glm.mse()

max_iterations

Maximum number of iterations

Type: int (default: -1).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> train, valid = cars.split_frame(ratios=[.8])
>>> cars_glm = H2OGeneralizedLinearEstimator(family='binomial',
...                                          max_iterations=50)
>>> cars_glm.train(x=predictors,
...                y=response,
...                training_frame=train,
...                validation_frame=valid)
>>> cars_glm.mse()

max_runtime_secs

Maximum allowed runtime in seconds for model training. Use 0 to disable.

Type: float (default: 0).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> train, valid = cars.split_frame(ratios=[.8])
>>> cars_glm = H2OGeneralizedLinearEstimator(max_runtime_secs=10,
...                                          seed=1234) 
>>> cars_glm.train(x=predictors,
...                y=response,
...                training_frame=train,
...                validation_frame=valid)
>>> cars_glm.mse()

missing_values_handling

Handling of missing values. Either MeanImputation, Skip or PlugValues.

One of: "mean_imputation", "skip", "plug_values" (default: "mean_imputation").

Examples:

>>> boston = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv")
>>> predictors = boston.columns[:-1]
>>> response = "medv"
>>> boston['chas'] = boston['chas'].asfactor()
>>> boston.insert_missing_values()
>>> train, valid = boston.split_frame(ratios=[.8])
>>> boston_glm = H2OGeneralizedLinearEstimator(missing_values_handling="skip")
>>> boston_glm.train(x=predictors,
...                  y=response,
...                  training_frame=train,
...                  validation_frame=valid)
>>> boston_glm.mse()

nfolds

Number of folds for K-fold cross-validation (0 to disable or >= 2).

Type: int (default: 0).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> folds = 5
>>> cars_glm = H2OGeneralizedLinearEstimator(nfolds=folds,
...                                          seed=1234,
...                                          family='binomial')
>>> cars_glm.train(x=predictors,
...                y=response,
...                training_frame=cars)
>>> cars_glm.auc(xval=True)

nlambdas

Number of lambdas to be used in a search. Default indicates: If alpha is zero, with lambda search set to True, the value of nlamdas is set to 30 (fewer lambdas are needed for ridge regression) otherwise it is set to 100.

Type: int (default: -1).

Examples:

>>> boston = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv")
>>> predictors = boston.columns[:-1]
>>> response = "medv"
>>> boston['chas'] = boston['chas'].asfactor()
>>> train, valid = boston.split_frame(ratios=[.8])
>>> boston_glm = H2OGeneralizedLinearEstimator(lambda_search=True,
...                                            nlambdas=50)
>>> boston_glm.train(x=predictors,
...                  y=response,
...                  training_frame=train,
...                  validation_frame=valid)
>>> print(boston_glm.mse(valid=True))

non_negative

Restrict coefficients (not intercept) to be non-negative

Type: bool (default: False).

Examples:

>>> airlines = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> airlines["Year"] = airlines["Year"].asfactor()
>>> airlines["Month"] = airlines["Month"].asfactor()
>>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
>>> airlines["Cancelled"] = airlines["Cancelled"].asfactor()
>>> airlines['FlightNum'] = airlines['FlightNum'].asfactor()
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> response = "IsDepDelayed"
>>> train, valid= airlines.split_frame(ratios=[.8])
>>> airlines_glm = H2OGeneralizedLinearEstimator(family='binomial',
...                                              non_negative=True)
>>> airlines_glm.train(x=predictors,
...                    y=response,
...                    training_frame=train,
...                    validation_frame=valid)
>>> airlines_glm.auc()

obj_reg

Likelihood divider in objective value computation, default is 1/nobs

Type: float (default: -1).

Examples:

>>> df = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/bigdata/laptop/glm_ordinal_logit/ordinal_multinomial_training_set.csv")
>>> df["C11"] = df["C11"].asfactor()
>>> ordinal_fit = H2OGeneralizedLinearEstimator(family="ordinal",
...                                             alpha=1.0,
...                                             lambda_=0.000000001,
...                                             obj_reg=0.00001,
...                                             max_iterations=1000,
...                                             beta_epsilon=1e-8,
...                                             objective_epsilon=1e-10)
>>> ordinal_fit.train(x=list(range(0,10)),
...                   y="C11",
...                   training_frame=df)
>>> ordinal_fit.mse()

objective_epsilon

Converge if objective value changes less than this. Default indicates: If lambda_search is set to True the value of objective_epsilon is set to .0001. If the lambda_search is set to False and lambda is equal to zero, the value of objective_epsilon is set to .000001, for any other value of lambda the default value of objective_epsilon is set to .0001.

Type: float (default: -1).

Examples:

>>> boston = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv")
>>> predictors = boston.columns[:-1]
>>> response = "medv"
>>> boston['chas'] = boston['chas'].asfactor()
>>> train, valid = boston.split_frame(ratios=[.8])
>>> boston_glm = H2OGeneralizedLinearEstimator(objective_epsilon=1e-3)
>>> boston_glm.train(x=predictors,
...                  y=response,
...                  training_frame=train,
...                  validation_frame=valid)
>>> boston_glm.mse()

offset_column

Offset column. This will be added to the combination of columns before applying the link function.

Type: str.

Examples:

>>> boston = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv")
>>> predictors = boston.columns[:-1]
>>> response = "medv"
>>> boston['chas'] = boston['chas'].asfactor()
>>> boston["offset"] = boston["medv"].log()
>>> train, valid = boston.split_frame(ratios=[.8], seed=1234)
>>> boston_glm = H2OGeneralizedLinearEstimator(offset_column="offset",
...                                            seed=1234)
>>> boston_glm.train(x=predictors,
...                  y=response,
...                  training_frame=train,
...                  validation_frame=valid)
>>> boston_glm.mse(valid=True)

plug_values

Plug Values (a single row frame containing values that will be used to impute missing values of the training/validation frame, use with conjunction missing_values_handling = PlugValues)

Type: H2OFrame.

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars = cars.drop(0)
>>> means = cars.mean()
>>> means = H2OFrame._expr(ExprNode("mean", cars, True, 0))
>>> glm_means = H2OGeneralizedLinearEstimator(seed=42)
>>> glm_means.train(training_frame=cars, y="cylinders")
>>> glm_plugs1 = H2OGeneralizedLinearEstimator(seed=42,
...                                            missing_values_handling="PlugValues",
...                                            plug_values=means)
>>> glm_plugs1.train(training_frame=cars, y="cylinders")
>>> glm_means.coef() == glm_plugs1.coef()
>>> not_means = 0.1 + (means * 0.5)
>>> glm_plugs2 = H2OGeneralizedLinearEstimator(seed=42,
...                                            missing_values_handling="PlugValues",
...                                            plug_values=not_means)
>>> glm_plugs2.train(training_frame=cars, y="cylinders")
>>> glm_means.coef() != glm_plugs2.coef()

prior

Prior probability for y==1. To be used only for logistic regression iff the data has been sampled and the mean of response does not reflect reality.

Type: float (default: -1).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> train, valid = cars.split_frame(ratios=[.8])
>>> cars_glm1 = H2OGeneralizedLinearEstimator(family='binomial', prior=0.5)
>>> cars_glm1.train(x=predictors,
...                 y=response,
...                 training_frame=train,
...                 validation_frame=valid)
>>> cars_glm1.mse()

rand_family

Random Component Family array. One for each random component. Only support gaussian for now.

Type: List[Enum["[gaussian]"]].

rand_link

Link function array for random component in HGLM.

Type: List[Enum["[identity]", "[family_default]"]].

random_columns

random columns indices for HGLM.

Type: List[int].

remove_collinear_columns

In case of linearly dependent columns, remove some of the dependent columns

Type: bool (default: False).

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> airlines["Year"] = airlines["Year"].asfactor()
>>> airlines["Month"] = airlines["Month"].asfactor()
>>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
>>> airlines["Cancelled"] = airlines["Cancelled"].asfactor()
>>> airlines['FlightNum'] = airlines['FlightNum'].asfactor()
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> response = "IsDepDelayed"
>>> train, valid = airlines.split_frame(ratios=[.8])
>>> airlines_glm = H2OGeneralizedLinearEstimator(family='binomial',
...                                              lambda_=0,
...                                              remove_collinear_columns=True)
>>> airlines_glm.train(x=predictors,
...                    y=response,
...                    training_frame=train,
...                    validation_frame=valid)
>>> airlines_glm.auc()

response_column

Response variable column.

Type: str.

score_each_iteration

Whether to score during each iteration of model training.

Type: bool (default: False).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_glm = H2OGeneralizedLinearEstimator(score_each_iteration=True,
...                                          seed=1234,
...                                          family='binomial')
>>> cars_glm.train(x=predictors,
...                y=response,
...                training_frame=train,
...                validation_frame=valid)
>>> cars_glm.scoring_history()

seed

Seed for pseudo random number generator (if applicable)

Type: int (default: -1).

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> airlines["Year"] = airlines["Year"].asfactor()
>>> airlines["Month"] = airlines["Month"].asfactor()
>>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
>>> airlines["Cancelled"] = airlines["Cancelled"].asfactor()
>>> airlines['FlightNum'] = airlines['FlightNum'].asfactor()
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> response = "IsDepDelayed"
>>> train, valid = airlines.split_frame(ratios=[.8], seed=1234)
>>> glm_w_seed = H2OGeneralizedLinearEstimator(family='binomial',
...                                            seed=1234)
>>> glm_w_seed.train(x=predictors,
...                  y=response,
...                  training_frame=train,
...                  validation_frame=valid)
>>> print(glm_w_seed_1.auc(valid=True))

solver

AUTO will set the solver based on given data and the other parameters. IRLSM is fast on on problems with small number of predictors and for lambda-search with L1 penalty, L_BFGS scales better for datasets with many columns.

One of: "auto", "irlsm", "l_bfgs", "coordinate_descent_naive", "coordinate_descent", "gradient_descent_lh", "gradient_descent_sqerr" (default: "auto").

Examples:

>>> boston = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv")
>>> predictors = boston.columns[:-1]
>>> response = "medv"
>>> boston['chas'] = boston['chas'].asfactor()
>>> train, valid = boston.split_frame(ratios=[.8])
>>> boston_glm = H2OGeneralizedLinearEstimator(solver='irlsm')
>>> boston_glm.train(x=predictors,
...                  y=response,
...                  training_frame=train,
...                  validation_frame=valid)
>>> print(boston_glm.mse(valid=True))

standardize

Standardize numeric columns to have zero mean and unit variance

Type: bool (default: True).

Examples:

>>> boston = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv")
>>> predictors = boston.columns[:-1]
>>> response = "medv"
>>> boston['chas'] = boston['chas'].asfactor()
>>> train, valid = boston.split_frame(ratios=[.8])
>>> boston_glm = H2OGeneralizedLinearEstimator(standardize=True)
>>> boston_glm.train(x=predictors,
...                  y=response,
...                  training_frame=train,
...                  validation_frame=valid)
>>> boston_glm.mse()

startval

double array to initialize fixed and random coefficients for HGLM.

Type: List[float].

theta

Theta

Type: float (default: 1e-10).

Examples:

>>> h2o_df = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/glm_test/Motor_insurance_sweden.txt")
>>> predictors = ["Payment", "Insured", "Kilometres", "Zone", "Bonus", "Make"]
>>> response = "Claims"
>>> negativebinomial_fit = H2OGeneralizedLinearEstimator(family="negativebinomial",
...                                                      link="identity",
...                                                      theta=0.5)
>>> negativebinomial_fit.train(x=predictors,
...                            y=response,
...                            training_frame=h2o_df)
>>> negativebinomial_fit.scoring_history()

training_frame

Id of the training data frame.

Type: H2OFrame.

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> train, valid = cars.split_frame(ratios=[.8],
...                                 seed=1234)
>>> cars_glm = H2OGeneralizedLinearEstimator(seed=1234,
...                                          family='binomial')
>>> cars_glm.train(x=predictors,
...                y=response,
...                training_frame=train,
...                validation_frame=valid)
>>> cars_glm.auc(train=True)

tweedie_link_power

Tweedie link power

Type: float (default: 1).

Examples:

>>> auto = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/auto.csv")
>>> predictors = auto.names
>>> predictors.remove('y')
>>> response = "y"
>>> train, valid = auto.split_frame(ratios=[.8])
>>> auto_glm = H2OGeneralizedLinearEstimator(family='tweedie',
...                                          tweedie_link_power=1)
>>> auto_glm.train(x=predictors,
...                y=response,
...                training_frame=train,
...                validation_frame=valid)
>>> print(auto_glm.mse(valid=True))

tweedie_variance_power

Tweedie variance power

Type: float (default: 0).

Examples:

>>> auto = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/auto.csv")
>>> predictors = auto.names
>>> predictors.remove('y')
>>> response = "y"
>>> train, valid = auto.split_frame(ratios=[.8])
>>> auto_glm = H2OGeneralizedLinearEstimator(family='tweedie',
...                                          tweedie_variance_power=1)
>>> auto_glm.train(x=predictors,
...                y=response,
...                training_frame=train,
...                validation_frame=valid)
>>> print(auto_glm.mse(valid=True))

validation_frame

Id of the validation data frame.

Type: H2OFrame.

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_glm = H2OGeneralizedLinearEstimator(seed=1234,
...                                          family='binomial')
>>> cars_glm.train(x=predictors,
...                y=response,
...                training_frame=train,
...                validation_frame=valid)
>>> cars_glm.auc(valid=True)

weights_column

Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor.

Type: str.

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","acceleration","year"]
>>> response = "economy_20mpg"
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_glm = H2OGeneralizedLinearEstimator(seed=1234,
...                                          family='binomial')
>>> cars_glm.train(x=predictors,
...                y=response,
...                training_frame=train,
...                validation_frame=valid,
...                weights_column="weight")
>>> cars_glm.auc(valid=True)

H2ONaiveBayesEstimator

class h2o.estimators.naive_bayes.H2ONaiveBayesEstimator(**kwargs)

Bases: h2o.estimators.estimator_base.H2OEstimator

Naive Bayes

The naive Bayes classifier assumes independence between predictor variables conditional on the response, and a Gaussian distribution of numeric predictors with mean and standard deviation computed from the training dataset. When building a naive Bayes classifier, every row in the training dataset that contains at least one NA will be skipped completely. If the test dataset has missing values, then those predictors are omitted in the probability calculation during prediction.

balance_classes

Balance training data class counts via over/under-sampling (for imbalanced data).

Type: bool (default: False).

Examples:

>>> iris = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_wheader.csv")
>>> iris_nb = H2ONaiveBayesEstimator(balance_classes=False,
...                                  nfolds=3,
...                                  seed=1234)
>>> iris_nb.train(x=list(range(4)),
...               y=4,
...               training_frame=iris)
>>> iris_nb.mse()

class_sampling_factors

Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will be automatically computed to obtain class balance during training. Requires balance_classes.

Type: List[float].

Examples:

>>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data")
>>> covtype[54] = covtype[54].asfactor()
>>> sample_factors = [1., 0.5, 1., 1., 1., 1., 1.]
>>> cov_nb = H2ONaiveBayesEstimator(class_sampling_factors=sample_factors,
...                                 seed=1234)
>>> predictors = covtype.columns[0:54]
>>> response = 'C55'
>>> cov_nb.train(x=predictors, y=response, training_frame=covtype)
>>> cov_nb.logloss()

compute_metrics

Compute metrics on training data

Type: bool (default: True).

Examples:

>>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv.zip")
>>> prostate['CAPSULE'] = prostate['CAPSULE'].asfactor()
>>> prostate['RACE'] = prostate['RACE'].asfactor()
>>> prostate['DCAPS'] = prostate['DCAPS'].asfactor()
>>> prostate['DPROS'] = prostate['DPROS'].asfactor()
>>> response_col = 'CAPSULE'
>>> prostate_nb = H2ONaiveBayesEstimator(laplace=0,
...                                      compute_metrics=False)
>>> prostate_nb.train(x=list(range(3,9)),
...                   y=response_col,
...                   training_frame=prostate)
>>> prostate_nb.show()

eps_prob

Cutoff below which probability is replaced with min_prob

Type: float (default: 0).

Examples:

>>> import random
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> problem = random.sample(["binomial","multinomial"],1)
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> if problem == "binomial":
...     response_col = "economy_20mpg"
... else:
...     response_col = "cylinders"
>>> cars[response_col] = cars[response_col].asfactor()
>>> cars_nb = H2ONaiveBayesEstimator(min_prob=0.1,
...                                  eps_prob=0.5,
...                                  seed=1234)
>>> cars_nb.train(x=predictors, y=response_col, training_frame=cars)
>>> cars_nb.mse()

eps_sdev

Cutoff below which standard deviation is replaced with min_sdev

Type: float (default: 0).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> problem = random.sample(["binomial","multinomial"],1)
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> if problem == "binomial":
...     response_col = "economy_20mpg"
... else:
...     response_col = "cylinders"
>>> cars[response_col] = cars[response_col].asfactor()
>>> cars_nb = H2ONaiveBayesEstimator(min_sdev=0.1,
...                                  eps_sdev=0.5,
...                                  seed=1234)
>>> cars_nb.train(x=predictors, y=response_col, training_frame=cars)
>>> cars_nb.mse()

export_checkpoints_dir

Automatically export generated models to this directory.

Type: str.

Examples:

>>> import tempfile
>>> from os import listdir
>>> airlines = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip", destination_frame="air.hex")
>>> predictors = ["DayofMonth", "DayOfWeek"]
>>> response = "IsDepDelayed"
>>> checkpoints_dir = tempfile.mkdtemp()
>>> air_nb = H2ONaiveBayesEstimator(export_checkpoints_dir=checkpoints_dir)
>>> air_nb.train(x=predictors, y=response, training_frame=airlines)
>>> len(listdir(checkpoints_dir))

fold_assignment

Cross-validation fold assignment scheme, if fold_column is not specified. The ‘Stratified’ option will stratify the folds based on the response variable, for classification problems.

One of: "auto", "random", "modulo", "stratified" (default: "auto").

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "cylinders"
>>> cars_nb = H2ONaiveBayesEstimator(fold_assignment="Random",
...                                  nfolds=5,
...                                  seed=1234)
>>> response = "economy_20mpg"
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> cars_nb.train(x=predictors, y=response, training_frame=cars)
>>> cars_nb.auc()

fold_column

Column with cross-validation fold index assignment per observation.

Type: str.

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> fold_numbers = cars.kfold_column(n_folds=5, seed=1234)
>>> fold_numbers.set_names(["fold_numbers"])
>>> cars = cars.cbind(fold_numbers)
>>> cars_nb = H2ONaiveBayesEstimator(seed=1234)
>>> cars_nb.train(x=predictors,
...               y=response,
...               training_frame=cars,
...               fold_column="fold_numbers")
>>> cars_nb.auc()

ignore_const_cols

Ignore constant columns.

Type: bool (default: True).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> cars["const_1"] = 6
>>> cars["const_2"] = 7
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_nb = H2ONaiveBayesEstimator(seed=1234,
...                                  ignore_const_cols=True)
>>> cars_nb.train(x=predictors,
...               y=response,
...               training_frame=train,
...               validation_frame=valid)
>>> cars_nb.auc()

ignored_columns

Names of columns to ignore for training.

Type: List[str].

keep_cross_validation_fold_assignment

Whether to keep the cross-validation fold assignment.

Type: bool (default: False).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_nb = H2ONaiveBayesEstimator(keep_cross_validation_fold_assignment=True,
...                                  nfolds=5,
...                                  seed=1234)
>>> cars_nb.train(x=predictors,
...               y=response,
...               training_frame=train)
>>> cars_nb.cross_validation_fold_assignment()

keep_cross_validation_models

Whether to keep the cross-validation models.

Type: bool (default: True).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_nb = H2ONaiveBayesEstimator(keep_cross_validation_models=True,
...                                  nfolds=5,
...                                  seed=1234)
>>> cars_nb.train(x=predictors,
...               y=response,
...               training_frame=train)
>>> cars_nb.cross_validation_models()

keep_cross_validation_predictions

Whether to keep the predictions of the cross-validation models.

Type: bool (default: False).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_nb = H2ONaiveBayesEstimator(keep_cross_validation_predictions=True,
...                                  nfolds=5,
...                                  seed=1234)
>>> cars_nb.train(x=predictors,
...               y=response,
...               training_frame=train)
>>> cars_nb.cross_validation_predictions()

laplace

Laplace smoothing parameter

Type: float (default: 0).

Examples:

>>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv.zip")
>>> prostate['CAPSULE'] = prostate['CAPSULE'].asfactor()
>>> prostate['RACE'] = prostate['RACE'].asfactor()
>>> prostate['DCAPS'] = prostate['DCAPS'].asfactor()
>>> prostate['DPROS'] = prostate['DPROS'].asfactor()
>>> prostate_nb = H2ONaiveBayesEstimator(laplace=1)
>>> prostate_nb.train(x=list(range(3,9)),
...                   y=response_col,
...                   training_frame=prostate)
>>> prostate_nb.mse()

max_after_balance_size

Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires balance_classes.

Type: float (default: 5).

Examples:

>>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data")
>>> covtype[54] = covtype[54].asfactor()
>>> predictors = covtype.columns[0:54]
>>> response = 'C55'
>>> train, valid = covtype.split_frame(ratios=[.8], seed=1234)
>>> max = .85
>>> cov_nb = H2ONaiveBayesEstimator(max_after_balance_size=max,
...                                 seed=1234) 
>>> cov_nb.train(x=predictors,
...              y=response,
...              training_frame=train,
...              validation_frame=valid)
>>> cars_nb.logloss()

max_confusion_matrix_size

[Deprecated] Maximum size (# classes) for confusion matrices to be printed in the Logs

Type: int (default: 20).

max_hit_ratio_k

Max. number (top K) of predictions to use for hit ratio computation (for multi-class only, 0 to disable)

Type: int (default: 0).

Examples:

>>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data")
>>> covtype[54] = covtype[54].asfactor()
>>> predictors = covtype.columns[0:54]
>>> response = 'C55'
>>> train, valid = covtype.split_frame(ratios=[.8], seed=1234)
>>> cars_nb = H2ONaiveBayesEstimator(max_hit_ratio_k=3,
...                                  seed=1234)
>>> cars_nb.train(x=predictors,
...               y=response,
...               training_frame=train,
...               validation_frame=valid)
>>> cov_nb.mse()

max_runtime_secs

Maximum allowed runtime in seconds for model training. Use 0 to disable.

Type: float (default: 0).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_nb = H2ONaiveBayesEstimator(max_runtime_secs=10,
...                                  seed=1234) 
>>> cars_nb.train(x=predictors,
...               y=response,
...               training_frame=train,
...               validation_frame=valid)
>>> cars_nb.auc()

min_prob

Min. probability to use for observations with not enough data

Type: float (default: 0.001).

Examples:

>>> import random
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> problem = random.sample(["binomial","multinomial"],1)
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> if problem == "binomial":
...     response_col = "economy_20mpg"
... else:
...     response_col = "cylinders"
>>> cars[response_col] = cars[response_col].asfactor()
>>> cars_nb = H2ONaiveBayesEstimator(min_prob=0.1,
...                                  eps_prob=0.5,
...                                  seed=1234)
>>> cars_nb.train(x=predictors,
...               y=response_col,
...               training_frame=cars)
>>> cars_nb.show()

min_sdev

Min. standard deviation to use for observations with not enough data

Type: float (default: 0.001).

Examples:

>>> import random
>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> problem = random.sample(["binomial","multinomial"],1)
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> if problem == "binomial":
...     response_col = "economy_20mpg"
... else:
...     response_col = "cylinders"
>>> cars[response_col] = cars[response_col].asfactor()
>>> cars_nb = H2ONaiveBayesEstimator(min_sdev=0.1,
...                                  eps_sdev=0.5,
...                                  seed=1234)
>>> cars_nb.train(x=predictors,
...               y=response_col,
...               training_frame=cars)
>>> cars_nb.show()

nfolds

Number of folds for K-fold cross-validation (0 to disable or >= 2).

Type: int (default: 0).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> cars_nb = H2ONaiveBayesEstimator(nfolds=5,
...                                  seed=1234)
>>> cars_nb.train(x=predictors,
...               y=response,
...               training_frame=cars)
>>> cars_nb.auc()

response_column

Response variable column.

Type: str.

score_each_iteration

Whether to score during each iteration of model training.

Type: bool (default: False).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_nb = H2ONaiveBayesEstimator(score_each_iteration=True,
...                                  seed=1234)
>>> cars_nb.train(x=predictors,
...               y=response,
...               training_frame=train,
...               validation_frame=valid)
>>> cars_nb.auc()

seed

Seed for pseudo random number generator (only used for cross-validation and fold_assignment=”Random” or “AUTO”)

Type: int (default: -1).

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> airlines["Year"] = airlines["Year"].asfactor()
>>> airlines["Month"] = airlines["Month"].asfactor()
>>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
>>> airlines["Cancelled"] = airlines["Cancelled"].asfactor()
>>> airlines['FlightNum'] = airlines['FlightNum'].asfactor()
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> response = "IsDepDelayed"
>>> train, valid= airlines.split_frame(ratios=[.8], seed=1234)
>>> nb_w_seed = H2ONaiveBayesEstimator(seed=1234)
>>> nb_w_seed.train(x=predictors,
...                 y=response,
...                 training_frame=train,
...                  validation_frame=valid)
>>> nb_wo_seed = H2ONaiveBayesEstimator()
>>> nb_wo_seed.train(x=predictors,
...                  y=response,
...                  training_frame=train,
...                  validation_frame=valid)
>>> nb_w_seed.auc()
>>> nb_wo_seed.auc()

training_frame

Id of the training data frame.

Type: H2OFrame.

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_nb = H2ONaiveBayesEstimator()
>>> cars_nb.train(x=predictors,
...               y=response,
...               training_frame=train,
...               validation_frame=valid)
>>> cars_nb.auc()

validation_frame

Id of the validation data frame.

Type: H2OFrame.

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_nb = H2ONaiveBayesEstimator()
>>> cars_nb.train(x=predictors,
...               y=response,
...               training_frame=train,
...               validation_frame=valid)
>>> cars_nb.auc()

H2OSupportVectorMachineEstimator

class h2o.estimators.psvm.H2OSupportVectorMachineEstimator(**kwargs)

Bases: h2o.estimators.estimator_base.H2OEstimator

PSVM

disable_training_metrics

Disable calculating training metrics (expensive on large datasets)

Type: bool (default: True).

Examples:

>>> from h2o.estimators import H2OSupportVectorMachineEstimator
>>> splice = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/splice/splice.svm")
>>> svm = H2OSupportVectorMachineEstimator(gamma=0.01,
...                                        rank_ratio=0.1,
...                                        disable_training_metrics=False)
>>> svm.train(y="C1", training_frame=splice)
>>> svm.mse()

fact_threshold

Convergence threshold of the Incomplete Cholesky Factorization (ICF)

Type: float (default: 1e-05).

Examples:

>>> splice = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/splice/splice.svm")
>>> svm = H2OSupportVectorMachineEstimator(disable_training_metrics=False,
...                                        fact_threshold=1e-7)
>>> svm.train(y="C1", training_frame=splice)
>>> svm.mse()

feasible_threshold

Convergence threshold for primal-dual residuals in the IPM iteration

Type: float (default: 0.001).

Examples:

>>> splice = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/splice/splice.svm")
>>> svm = H2OSupportVectorMachineEstimator(disable_training_metrics=False,
...                                        fact_threshold=1e-7)
>>> svm.train(y="C1", training_frame=splice)
>>> svm.mse()

gamma

Coefficient of the kernel (currently RBF gamma for gaussian kernel, -1 means 1/#features)

Type: float (default: -1).

Examples:

>>> splice = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/splice/splice.svm")
>>> svm = H2OSupportVectorMachineEstimator(gamma=0.01,
...                                        rank_ratio=0.1,
...                                        disable_training_metrics=False)
>>> svm.train(y="C1", training_frame=splice)
>>> svm.mse()

hyper_param

Penalty parameter C of the error term

Type: float (default: 1).

Examples:

>>> splice = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/splice/splice.svm")
>>> svm = H2OSupportVectorMachineEstimator(gamma=0.01,
...                                        rank_ratio=0.1,
...                                        hyper_param=0.01,
...                                        disable_training_metrics=False)
>>> svm.train(y="C1", training_frame=splice)
>>> svm.mse()

ignore_const_cols

Ignore constant columns.

Type: bool (default: True).

Examples:

>>> splice = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/splice/splice.svm")
>>> svm = H2OSupportVectorMachineEstimator(gamma=0.01,
...                                        rank_ratio=0.1,
...                                        ignore_const_cols=False,
...                                        disable_training_metrics=False)
>>> svm.train(y="C1", training_frame=splice)
>>> svm.mse()

ignored_columns

Names of columns to ignore for training.

Type: List[str].

kernel_type

Type of used kernel

One of: "gaussian" (default: "gaussian").

Examples:

>>> splice = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/splice/splice.svm")
>>> svm = H2OSupportVectorMachineEstimator(gamma=0.1,
...                                        rank_ratio=0.1,
...                                        hyper_param=0.01,
...                                        kernel_type="gaussian",
...                                        disable_training_metrics=False)
>>> svm.train(y="C1", training_frame=splice) 
>>> svm.mse()

max_iterations

Maximum number of iteration of the algorithm

Type: int (default: 200).

Examples:

>>> splice = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/splice/splice.svm")
>>> svm = H2OSupportVectorMachineEstimator(gamma=0.1,
...                                        rank_ratio=0.1,
...                                        hyper_param=0.01,
...                                        max_iterations=20,
...                                        disable_training_metrics=False)
>>> svm.train(y="C1", training_frame=splice)  
>>> svm.mse()

mu_factor

Increasing factor mu

Type: float (default: 10).

Examples:

>>> splice = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/splice/splice.svm")
>>> svm = H2OSupportVectorMachineEstimator(gamma=0.1,
...                                        mu_factor=100.5,
...                                        disable_training_metrics=False)
>>> svm.train(y="C1", training_frame=splice) 
>>> svm.mse()

negative_weight

Weight of positive (-1) class of observations

Type: float (default: 1).

Examples:

>>> splice = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/splice/splice.svm")
>>> svm = H2OSupportVectorMachineEstimator(gamma=0.1,
...                                        rank_ratio=0.1,
...                                        negative_weight=10,
...                                        disable_training_metrics=False)
>>> svm.train(y="C1", training_frame=splice)  
>>> svm.mse()

positive_weight

Weight of positive (+1) class of observations

Type: float (default: 1).

Examples:

>>> splice = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/splice/splice.svm")
>>> svm = H2OSupportVectorMachineEstimator(gamma=0.1,
...                                        rank_ratio=0.1,
...                                        positive_weight=0.1,
...                                        disable_training_metrics=False)
>>> svm.train(y="C1", training_frame=splice)   
>>> svm.mse()

rank_ratio

Desired rank of the ICF matrix expressed as an ration of number of input rows (-1 means use sqrt(#rows)).

Type: float (default: -1).

Examples:

>>> splice = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/splice/splice.svm")
>>> svm = H2OSupportVectorMachineEstimator(gamma=0.01,
...                                        rank_ratio=0.1,
...                                        disable_training_metrics=False)
>>> svm.train(y="C1", training_frame=splice)
>>> svm.mse()

response_column

Response variable column.

Type: str.

seed

Seed for pseudo random number generator (if applicable)

Type: int (default: -1).

Examples:

>>> splice = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/splice/splice.svm")
>>> svm = H2OSupportVectorMachineEstimator(gamma=0.1,
...                                        rank_ratio=0.1,
...                                        seed=1234,
...                                        disable_training_metrics=False)
>>> svm.train(y="C1", training_frame=splice)
>>> svm.model_performance

surrogate_gap_threshold

Feasibility criterion of the surrogate duality gap (eta)

Type: float (default: 0.001).

Examples:

>>> splice = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/splice/splice.svm")
>>> svm = H2OSupportVectorMachineEstimator(gamma=0.01,
...                                        rank_ratio=0.1,
...                                        surrogate_gap_threshold=0.1,
...                                        disable_training_metrics=False)
>>> svm.train(y="C1", training_frame=splice) 
>>> svm.mse()

sv_threshold

Threshold for accepting a candidate observation into the set of support vectors

Type: float (default: 0.0001).

Examples:

>>> splice = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/splice/splice.svm")
>>> svm = H2OSupportVectorMachineEstimator(gamma=0.01,
...                                        rank_ratio=0.1,
...                                        sv_threshold=0.01,
...                                        disable_training_metrics=False)
>>> svm.train(y="C1", training_frame=splice) 
>>> svm.mse()

training_frame

Id of the training data frame.

Type: H2OFrame.

Examples:

>>> splice = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/splice/splice.svm")
>>> train, valid = splice.split_frame(ratios=[0.8])
>>> svm = H2OSupportVectorMachineEstimator(disable_training_metrics=False)
>>> svm.train(y="C1", training_frame=train)
>>> svm.mse()

validation_frame

Id of the validation data frame.

Type: H2OFrame.

Examples:

>>> splice = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/splice/splice.svm")
>>> train, valid = splice.split_frame(ratios=[0.8])
>>> svm = H2OSupportVectorMachineEstimator(disable_training_metrics=False)
>>> svm.train(y="C1", training_frame=train, validation_frame=valid)
>>> svm.mse()

H2ORandomForestEstimator

class h2o.estimators.random_forest.H2ORandomForestEstimator(**kwargs)

Bases: h2o.estimators.estimator_base.H2OEstimator

Distributed Random Forest

Builds a Distributed Random Forest (DRF) on a parsed dataset, for regression or classification.

balance_classes

Balance training data class counts via over/under-sampling (for imbalanced data).

Type: bool (default: False).

Examples:

>>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data")
>>> covtype[54] = covtype[54].asfactor()
>>> predictors = covtype.columns[0:54]
>>> response = 'C55'
>>> train, valid = covtype.split_frame(ratios=[.8], seed=1234)
>>> cov_drf = H2ORandomForestEstimator(balance_classes=True,
...                                    seed=1234)
>>> cov_drf.train(x=predictors,
...               y=response,
...               training_frame=train,
...               validation_frame=valid)
>>> print('logloss', cov_drf.logloss(valid=True))

binomial_double_trees

For binary classification: Build 2x as many trees (one per class) - can lead to higher accuracy.

Type: bool (default: False).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_drf = H2ORandomForestEstimator(binomial_double_trees=False,
...                                     seed=1234)
>>> cars_drf.train(x=predictors,
...                y=response,
...                training_frame=train,
...                validation_frame=valid)
>>> print('without binomial_double_trees:',
...        cars_drf.auc(valid=True))
>>> cars_drf_2 = H2ORandomForestEstimator(binomial_double_trees=True,
...                                       seed=1234)
>>> cars_drf_2.train(x=predictors,
...                  y=response,
...                  training_frame=train,
...                  validation_frame=valid)
>>> print('with binomial_double_trees:', cars_drf_2.auc(valid=True))

build_tree_one_node

Run on one node only; no network overhead but fewer cpus used. Suitable for small datasets.

Type: bool (default: False).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_drf = H2ORandomForestEstimator(build_tree_one_node=True,
...                                     seed=1234)
>>> cars_drf.train(x=predictors,
...                y=response,
...                training_frame=train,
...                validation_frame=valid)
>>> cars_drf.auc(valid=True)

calibrate_model

Use Platt Scaling to calculate calibrated class probabilities. Calibration can provide more accurate estimates of class probabilities.

Type: bool (default: False).

Examples:

>>> ecology = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/ecology_model.csv")
>>> ecology['Angaus'] = ecology['Angaus'].asfactor()
>>> from h2o.estimators.random_forest import H2ORandomForestEstimator
>>> response = 'Angaus'
>>> predictors = ecology.columns[3:13]
>>> train, calib = ecology.split_frame(seed=12354)
>>> w = h2o.create_frame(binary_fraction=1,
...                      binary_ones_fraction=0.5,
...                      missing_fraction=0,
...                      rows=744, cols=1)
>>> w.set_names(["weight"])
>>> train = train.cbind(w)
>>> ecology_drf = H2ORandomForestEstimator(ntrees=10,
...                                        max_depth=5,
...                                        min_rows=10,
...                                        distribution="multinomial",
...                                        weights_column="weight",
...                                        calibrate_model=True,
...                                        calibration_frame=calib)
>>> ecology_drf.train(x=predictors,
...                   y="Angaus",
...                   training_frame=train)
>>> predicted = ecology_drf.predict(calib)

calibration_frame

Calibration frame for Platt Scaling

Type: H2OFrame.

Examples:

>>> ecology = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/ecology_model.csv")
>>> ecology['Angaus'] = ecology['Angaus'].asfactor()
>>> response = 'Angaus'
>>> predictors = ecology.columns[3:13]
>>> train, calib = ecology.split_frame(seed = 12354)
>>> w = h2o.create_frame(binary_fraction=1,
...                      binary_ones_fraction=0.5,
...                      missing_fraction=0,
...                      rows=744, cols=1)
>>> w.set_names(["weight"])
>>> train = train.cbind(w)
>>> ecology_drf = H2ORandomForestEstimator(ntrees=10,
...                                        max_depth=5,
...                                        min_rows=10,
...                                        distribution="multinomial",
...                                        calibrate_model=True,
...                                        calibration_frame=calib)
>>> ecology_drf.train(x=predictors,
...                   y="Angaus,
...                   training_frame=train,
...                   weights_column="weight")
>>> predicted = ecology_drf.predict(train)

categorical_encoding

Encoding scheme for categorical features

One of: "auto", "enum", "one_hot_internal", "one_hot_explicit", "binary", "eigen", "label_encoder", "sort_by_response", "enum_limited" (default: "auto").

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") 
>>> airlines["Year"] = airlines["Year"].asfactor()
>>> airlines["Month"] = airlines["Month"].asfactor()
>>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
>>> airlines["Cancelled"] = airlines["Cancelled"].asfactor()
>>> airlines['FlightNum'] = airlines['FlightNum'].asfactor()
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> response = "IsDepDelayed"
>>> train, valid= airlines.split_frame(ratios=[.8], seed=1234)
>>> encoding = "one_hot_explicit"
>>> airlines_drf = H2ORandomForestEstimator(categorical_encoding=encoding,
...                                         seed=1234)
>>> airlines_drf.train(x=predictors,
...                    y=response,
...                    training_frame=train,
...                    validation_frame=valid)
>>> airlines_drf.auc(valid=True)

check_constant_response

Check if response column is constant. If enabled, then an exception is thrown if the response column is a constant value.If disabled, then model will train regardless of the response column being a constant value or not.

Type: bool (default: True).

Examples:

>>> train = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/iris/iris_train.csv")
>>> train["constantCol"] = 1
>>> my_drf = H2ORandomForestEstimator(check_constant_response=False)
>>> my_drf.train(x=list(range(1,5)),
...              y="constantCol",
...              training_frame=train)

checkpoint

Model checkpoint to resume training with.

Type: str.

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> train, valid = cars.split_frame(ratios=[.8],
...                                 seed=1234)
>>> cars_drf = H2ORandomForestEstimator(ntrees=1,
...                                     seed=1234)
>>> cars_drf.train(x=predictors,
...                y=response,
...                training_frame=train,
...                validation_frame=valid)
>>> print(cars_drf.auc(valid=True))

class_sampling_factors

Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will be automatically computed to obtain class balance during training. Requires balance_classes.

Type: List[float].

Examples:

>>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data")
>>> covtype[54] = covtype[54].asfactor()
>>> predictors = covtype.columns[0:54]
>>> response = 'C55'
>>> train, valid = covtype.split_frame(ratios=[.8], seed=1234)
>>> print(covtype[54].table())
>>> sample_factors = [1., 0.5, 1., 1., 1., 1., 1.]
>>> cov_drf = H2ORandomForestEstimator(balance_classes=True,
...                                    class_sampling_factors=sample_factors,
...                                    seed=1234)
>>> cov_drf.train(x=predictors,
...               y=response,
...               training_frame=train,
...               validation_frame=valid)
>>> print('logloss', cov_drf.logloss(valid=True))

col_sample_rate_change_per_level

Relative change of the column sampling rate for every level (must be > 0.0 and <= 2.0)

Type: float (default: 1).

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> airlines["Year"] = airlines["Year"].asfactor()
>>> airlines["Month"] = airlines["Month"].asfactor()
>>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
>>> airlines["Cancelled"] = airlines["Cancelled"].asfactor()
>>> airlines['FlightNum'] = airlines['FlightNum'].asfactor()
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> response = "IsDepDelayed"
>>> train, valid= airlines.split_frame(ratios=[.8], seed=1234)
>>> airlines_drf = H2ORandomForestEstimator(col_sample_rate_change_per_level=.9,
...                                         seed=1234)
>>> airlines_drf.train(x=predictors,
...                    y=response,
...                    training_frame=train,
...                    validation_frame=valid)
>>>  print(airlines_drf.auc(valid=True))

col_sample_rate_per_tree

Column sample rate per tree (from 0.0 to 1.0)

Type: float (default: 1).

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> airlines["Year"] = airlines["Year"].asfactor()
>>> airlines["Month"] = airlines["Month"].asfactor()
>>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
>>> airlines["Cancelled"] = airlines["Cancelled"].asfactor()
>>> airlines['FlightNum'] = airlines['FlightNum'].asfactor()
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> response = "IsDepDelayed"
>>> train, valid= airlines.split_frame(ratios=[.8], seed=1234)
>>> airlines_drf = H2ORandomForestEstimator(col_sample_rate_per_tree=.7,
...                                         seed=1234)
>>> airlines_drf.train(x=predictors,
...                    y=response,
...                    training_frame=train,
...                    validation_frame=valid)
>>> print(airlines_drf.auc(valid=True))

custom_metric_func

Reference to custom evaluation function, format: language:keyName=funcName

Type: str.

distribution

[Deprecated] Distribution function

One of: "auto", "bernoulli", "multinomial", "gaussian", "poisson", "gamma", "tweedie", "laplace", "quantile", "huber" (default: "auto").

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "cylinders"
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_drf = H2ORandomForestEstimator(distribution="poisson",
...                                     seed=1234)
>>> cars_drf.train(x=predictors,
...                y=response,
...                training_frame=train,
...                validation_frame=valid)
>>> cars_drf.mse(valid=True)

export_checkpoints_dir

Automatically export generated models to this directory.

Type: str.

Examples:

>>> import tempfile
>>> from os import listdir
>>> from h2o.grid.grid_search import H2OGridSearch
>>> airlines = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip", destination_frame="air.hex")
>>> predictors = ["DayofMonth", "DayOfWeek"]
>>> response = "IsDepDelayed"
>>> hyper_parameters = {'ntrees': [5,10]}
>>> search_crit = {'strategy': "RandomDiscrete",
...                'max_models': 5,
...                'seed': 1234,
...                'stopping_rounds': 3,
...                'stopping_metric': "AUTO",
...                'stopping_tolerance': 1e-2}
>>> checkpoints_dir = tempfile.mkdtemp()
>>> air_grid = H2OGridSearch(H2ORandomForestEstimator,
...                          hyper_params=hyper_parameters,
...                          search_criteria=search_crit)
>>> air_grid.train(x=predictors,
...                y=response,
...                training_frame=airlines,
...                distribution="bernoulli",
...                max_depth=3,
...                export_checkpoints_dir=checkpoints_dir)
>>> num_files = len(listdir(checkpoints_dir))
>>> num_files

fold_assignment

Cross-validation fold assignment scheme, if fold_column is not specified. The ‘Stratified’ option will stratify the folds based on the response variable, for classification problems.

One of: "auto", "random", "modulo", "stratified" (default: "auto").

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> assignment_type = "Random"
>>> cars_drf = H2ORandomForestEstimator(fold_assignment=assignment_type,
...                                     nfolds=5,
...                                     seed=1234)
>>> cars_drf.train(x=predictors,
...                y=response,
...                training_frame=cars)
>>> cars_drf.auc(xval=True)

fold_column

Column with cross-validation fold index assignment per observation.

Type: str.

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> fold_numbers = cars.kfold_column(n_folds=5, seed=1234)
>>> fold_numbers.set_names(["fold_numbers"])
>>> cars = cars.cbind(fold_numbers)
>>> print(cars['fold_numbers'])
>>> cars_drf = H2ORandomForestEstimator(seed=1234)
>>> cars_drf.train(x=predictors,
...                y=response,
...                training_frame=cars,
...                fold_column="fold_numbers")
>>> cars_drf.auc(xval=True)

histogram_type

What type of histogram to use for finding optimal split points

One of: "auto", "uniform_adaptive", "random", "quantiles_global", "round_robin" (default: "auto").

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> airlines["Year"] = airlines["Year"].asfactor()
>>> airlines["Month"] = airlines["Month"].asfactor()
>>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
>>> airlines["Cancelled"] = airlines["Cancelled"].asfactor()
>>> airlines['FlightNum'] = airlines['FlightNum'].asfactor()
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> response = "IsDepDelayed"
>>> train, valid= airlines.split_frame(ratios=[.8], seed=1234)
>>> airlines_drf = H2ORandomForestEstimator(histogram_type="UniformAdaptive",
...                                         seed=1234)
>>> airlines_drf.train(x=predictors,
...                    y=response,
...                    training_frame=train,
...                    validation_frame=valid)
>>> print(airlines_drf.auc(valid=True))

ignore_const_cols

Ignore constant columns.

Type: bool (default: True).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> cars["const_1"] = 6
>>> cars["const_2"] = 7
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_drf = H2ORandomForestEstimator(seed=1234,
...                                     ignore_const_cols=True)
>>> cars_drf.train(x=predictors,
...                y=response,
...                training_frame=train,
...                validation_frame=valid)
>>> cars_drf.auc(valid=True)

ignored_columns

Names of columns to ignore for training.

Type: List[str].

keep_cross_validation_fold_assignment

Whether to keep the cross-validation fold assignment.

Type: bool (default: False).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_drf = H2ORandomForestEstimator(keep_cross_validation_fold_assignment=True,
...                                     nfolds=5,
...                                     seed=1234)
>>> cars_drf.train(x=predictors,
...                y=response,
...                training_frame=train)
>>> cars_drf.cross_validation_fold_assignment()

keep_cross_validation_models

Whether to keep the cross-validation models.

Type: bool (default: True).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_drf = H2ORandomForestEstimator(keep_cross_validation_models=True,
...                                     nfolds=5,
...                                     seed=1234)
>>> cars_drf.train(x=predictors,
...                y=response,
...                training_frame=train)
>>> cars_drf.auc()

keep_cross_validation_predictions

Whether to keep the predictions of the cross-validation models.

Type: bool (default: False).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_drf = H2ORandomForestEstimator(keep_cross_validation_predictions=True,
...                                     nfolds=5,
...                                     seed=1234)
>>> cars_drf.train(x=predictors,
...                y=response,
...                training_frame=train)
>>> cars_drf.cross_validation_predictions()

max_after_balance_size

Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires balance_classes.

Type: float (default: 5).

Examples:

>>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data")
>>> covtype[54] = covtype[54].asfactor()
>>> predictors = covtype.columns[0:54]
>>> response = 'C55'
>>> train, valid = covtype.split_frame(ratios=[.8], seed=1234)
>>> print(covtype[54].table())
>>> max = .85
>>> cov_drf = H2ORandomForestEstimator(balance_classes=True,
...                                    max_after_balance_size=max,
...                                    seed=1234)
>>> cov_drf.train(x=predictors,
...               y=response,
...               training_frame=train,
...               validation_frame=valid)
>>> print('logloss', cov_drf.logloss(valid=True))

max_confusion_matrix_size

[Deprecated] Maximum size (# classes) for confusion matrices to be printed in the Logs

Type: int (default: 20).

max_depth

Maximum tree depth.

Type: int (default: 20).

Examples:

>>> df = h2o.import_file(path = "http://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv")
>>> response = "survived"
>>> df[response] = df[response].asfactor()
>>> predictors = df.columns
>>> del predictors[1:3]
>>> train, valid, test = df.split_frame(ratios=[0.6,0.2],
...                                     seed=1234,
...                                     destination_frames=
...                                     ['train.hex','valid.hex','test.hex'])
>>> drf = H2ORandomForestEstimator()
>>> drf.train(x=predictors,
...           y=response,
...           training_frame=train)
>>> perf = drf.model_performance(valid)
>>> print perf.auc()

max_hit_ratio_k

Max. number (top K) of predictions to use for hit ratio computation (for multi-class only, 0 to disable)

Type: int (default: 0).

Examples:

>>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data")
>>> covtype[54] = covtype[54].asfactor()
>>> predictors = covtype.columns[0:54]
>>> response = 'C55'
>>> train, valid = covtype.split_frame(ratios=[.8], seed=1234)
>>> cov_drf = H2ORandomForestEstimator(max_hit_ratio_k=3,
...                                    seed=1234)
>>> cov_drf.train(x=predictors,
...               y=response,
...               training_frame=train,
...               validation_frame=valid)
>>> cov_drf.show()

max_runtime_secs

Maximum allowed runtime in seconds for model training. Use 0 to disable.

Type: float (default: 0).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_drf = H2ORandomForestEstimator(max_runtime_secs=10,
...                                     ntrees=10000,
...                                     max_depth=10,
...                                     seed=1234)
>>> cars_drf.train(x=predictors,
...                y=response,
...                training_frame=train,
...                validation_frame=valid)
>>> cars_drf.auc(valid = True)

min_rows

Fewest allowed (weighted) observations in a leaf.

Type: float (default: 1).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_drf = H2ORandomForestEstimator(min_rows=16,
...                                     seed=1234)
>>> cars_drf.train(x=predictors,
...                y=response,
...                training_frame=train,
...                validation_frame=valid)
>>> print(cars_drf.auc(valid=True))

min_split_improvement

Minimum relative improvement in squared error reduction for a split to happen

Type: float (default: 1e-05).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_drf = H2ORandomForestEstimator(min_split_improvement=1e-3,
...                                     seed=1234)
>>> cars_drf.train(x=predictors,
...                y=response,
...                training_frame=train,
...                validation_frame=valid)
>>> print(cars_drf.auc(valid=True))

mtries

Number of variables randomly sampled as candidates at each split. If set to -1, defaults to sqrt{p} for classification and p/3 for regression (where p is the # of predictors

Type: int (default: -1).

Examples:

>>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data")
>>> covtype[54] = covtype[54].asfactor()
>>> predictors = covtype.columns[0:54]
>>> response = 'C55'
>>> train, valid = covtype.split_frame(ratios=[.8], seed=1234)
>>> cov_drf = H2ORandomForestEstimator(mtries=30, seed=1234)
>>> cov_drf.train(x=predictors,
...               y=response,
...               training_frame=train,
...               validation_frame=valid)
>>> print('logloss', cov_drf.logloss(valid=True))

nbins

For numerical columns (real/int), build a histogram of (at least) this many bins, then split at the best point

Type: int (default: 20).

Examples:

>>> eeg = h2o.import_file("https://h2o-public-test-data.s3.amazonaws.com/smalldata/eeg/eeg_eyestate.csv")
>>> eeg['eyeDetection'] = eeg['eyeDetection'].asfactor()
>>> predictors = eeg.columns[:-1]
>>> response = 'eyeDetection'
>>> train, valid = eeg.split_frame(ratios=[.8], seed=1234)
>>> bin_num = [16, 32, 64, 128, 256, 512]
>>> label = ["16", "32", "64", "128", "256", "512"]
>>> for key, num in enumerate(bin_num):
#              Insert integer for 'num' and 'key'
>>> eeg_drf = H2ORandomForestEstimator(nbins=num, seed=1234)
>>> eeg_drf.train(x=predictors,
...               y=response,
...               training_frame=train,
...               validation_frame=valid)
>>> print(label[key], 'training score',
...       eeg_drf.auc(train=True))
>>> print(label[key], 'validation score',
...       eeg_drf.auc(train=True))

nbins_cats

For categorical columns (factors), build a histogram of this many bins, then split at the best point. Higher values can lead to more overfitting.

Type: int (default: 1024).

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> airlines["Year"] = airlines["Year"].asfactor()
>>> airlines["Month"] = airlines["Month"].asfactor()
>>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
>>> airlines["Cancelled"] = airlines["Cancelled"].asfactor()
>>> airlines['FlightNum'] = airlines['FlightNum'].asfactor()
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> response = "IsDepDelayed"
>>> train, valid= airlines.split_frame(ratios=[.8], seed=1234)
>>> bin_num = [8, 16, 32, 64, 128, 256,
...            512, 1024, 2048, 4096]
>>> label = ["8", "16", "32", "64", "128",
...          "256", "512", "1024", "2048", "4096"]
>>> for key, num in enumerate(bin_num):
#              Insert integer for 'num' and 'key'
>>> airlines_drf = H2ORandomForestEstimator(nbins_cats=num,
...                                         seed=1234)
>>> airlines_drf.train(x=predictors,
...                    y=response,
...                    training_frame=train,
...                    validation_frame=valid)
>>> print(label[key], 'training score',
...       airlines_gbm.auc(train=True))
>>> print(label[key], 'validation score',
...       airlines_gbm.auc(valid=True))

nbins_top_level

For numerical columns (real/int), build a histogram of (at most) this many bins at the root level, then decrease by factor of two per level

Type: int (default: 1024).

Examples:

>>> eeg = h2o.import_file("https://h2o-public-test-data.s3.amazonaws.com/smalldata/eeg/eeg_eyestate.csv")
>>> eeg['eyeDetection'] = eeg['eyeDetection'].asfactor()
>>> predictors = eeg.columns[:-1]
>>> response = 'eyeDetection'
>>> train, valid = eeg.split_frame(ratios=[.8],
...                                seed=1234)
>>> bin_num = [32, 64, 128, 256, 512,
...            1024, 2048, 4096]
>>> label = ["32", "64", "128", "256",
...          "512", "1024", "2048", "4096"]
>>> for key, num in enumerate(bin_num):
#              Insert integer for 'num' and 'key'
>>> eeg_drf = H2ORandomForestEstimator(nbins_top_level=32,
...                                    seed=1234)
>>> eeg_drf.train(x=predictors,
...               y=response,
...               training_frame=train,
...               validation_frame=valid)
>>> print(label[key], 'training score',
...       eeg_gbm.auc(train=True))
>>> print(label[key], 'validation score',
...       eeg_gbm.auc(valid=True))

nfolds

Number of folds for K-fold cross-validation (0 to disable or >= 2).

Type: int (default: 0).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> folds = 5
>>> cars_drf = H2ORandomForestEstimator(nfolds=folds,
...                                     seed=1234)
>>> cars_drf.train(x=predictors,
...                y=response,
...                training_frame=cars)
>>> cars_drf.auc(xval=True)

ntrees

Number of trees.

Type: int (default: 50).

Examples:

>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv")
>>> titanic['survived'] = titanic['survived'].asfactor()
>>> predictors = titanic.columns
>>> del predictors[1:3]
>>> response = 'survived'
>>> train, valid = titanic.split_frame(ratios=[.8],
...                                    seed=1234)
>>> tree_num = [20, 50, 80, 110,
...             140, 170, 200]
>>> label = ["20", "50", "80", "110",
...          "140", "170", "200"]
>>> for key, num in enumerate(tree_num):
#              Input an integer for 'num' and 'key'
>>> titanic_drf = H2ORandomForestEstimator(ntrees=num,
...                                        seed=1234)
>>> titanic_drf.train(x=predictors,
...                   y=response,
...                   training_frame=train,
...                   validation_frame=valid)
>>> print(label[key], 'training score',
...       titanic_drf.auc(train=True))
>>> print(label[key], 'validation score',
...       titanic_drf.auc(valid=True))

offset_column

[Deprecated] Offset column. This will be added to the combination of columns before applying the link function.

Type: str.

r2_stopping

r2_stopping is no longer supported and will be ignored if set - please use stopping_rounds, stopping_metric and stopping_tolerance instead. Previous version of H2O would stop making trees when the R^2 metric equals or exceeds this

Type: float (default: 1.797693135e+308).

response_column

Response variable column.

Type: str.

sample_rate

Row sample rate per tree (from 0.0 to 1.0)

Type: float (default: 0.632).

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> airlines["Year"] = airlines["Year"].asfactor()
>>> airlines["Month"] = airlines["Month"].asfactor()
>>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
>>> airlines["Cancelled"] = airlines["Cancelled"].asfactor()
>>> airlines['FlightNum'] = airlines['FlightNum'].asfactor()
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> response = "IsDepDelayed"
>>> train, valid= airlines.split_frame(ratios=[.8],
...                                    seed=1234)
>>> airlines_drf = H2ORandomForestEstimator(sample_rate=.7,
...                                         seed=1234)
>>> airlines_drf.train(x=predictors,
...                    y=response,
...                    training_frame=train,
...                    validation_frame=valid)
>>> print(airlines_drf.auc(valid=True))

sample_rate_per_class

A list of row sample rates per class (relative fraction for each class, from 0.0 to 1.0), for each tree

Type: List[float].

Examples:

>>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data")
>>> covtype[54] = covtype[54].asfactor()
>>> predictors = covtype.columns[0:54]
>>> response = 'C55'
>>> train, valid = covtype.split_frame(ratios=[.8],
...                                    seed=1234)
>>> print(train[response].table())
>>> rate_per_class_list = [1, .4, 1, 1, 1, 1, 1]
>>> cov_drf = H2ORandomForestEstimator(sample_rate_per_class=rate_per_class_list,
...                                    seed=1234)
>>> cov_drf.train(x=predictors,
...               y=response,
...               training_frame=train,
...               validation_frame=valid)
>>> print('logloss', cov_drf.logloss(valid=True))

score_each_iteration

Whether to score during each iteration of model training.

Type: bool (default: False).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_drf = H2ORandomForestEstimator(score_each_iteration=True,
...                                     ntrees=55,
...                                     seed=1234)
>>> cars_drf.train(x=predictors,
...                y=response,
...                training_frame=train,
...                validation_frame = valid)
>>> cars_drf.scoring_history()

score_tree_interval

Score the model after every so many trees. Disabled if set to 0.

Type: int (default: 0).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_drf = H2ORandomForestEstimator(score_tree_interval=5,
...                                     seed=1234)
>>> cars_drf.train(x=predictors,
...                y=response,
...                training_frame=train,
...                validation_frame=valid)
>>> cars_drf.scoring_history()

seed

Seed for pseudo random number generator (if applicable)

Type: int (default: -1).

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> airlines["Year"] = airlines["Year"].asfactor()
>>> airlines["Month"] = airlines["Month"].asfactor()
>>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
>>> airlines["Cancelled"] = airlines["Cancelled"].asfactor()
>>> airlines['FlightNum'] = airlines['FlightNum'].asfactor()
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> response = "IsDepDelayed"
>>> train, valid= airlines.split_frame(ratios=[.8], seed=1234)
>>> drf_w_seed_1 = H2ORandomForestEstimator(seed=1234)
>>> drf_w_seed_1.train(x=predictors,
...                    y=response,
...                    training_frame=train,
...                    validation_frame=valid)
>>> print('auc for the 1st model build with a seed:',
...        drf_w_seed_1.auc(valid=True))

stopping_metric

Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anonomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client.

One of: "auto", "deviance", "logloss", "mse", "rmse", "mae", "rmsle", "auc", "aucpr", "lift_top_group", "misclassification", "mean_per_class_error", "custom", "custom_increasing" (default: "auto").

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> airlines["Year"] = airlines["Year"].asfactor()
>>> airlines["Month"] = airlines["Month"].asfactor()
>>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
>>> airlines["Cancelled"] = airlines["Cancelled"].asfactor()
>>> airlines['FlightNum'] = airlines['FlightNum'].asfactor()
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> response = "IsDepDelayed"
>>> train, valid= airlines.split_frame(ratios=[.8],
...                                    seed=1234)
>>> airlines_drf = H2ORandomForestEstimator(stopping_metric="auc",
...                                         stopping_rounds=3,
...                                         stopping_tolerance=1e-2,
...                                         seed=1234)
>>> airlines_drf.train(x=predictors,
...                    y=response,
...                    training_frame=train,
...                    validation_frame=valid)
>>> airlines_drf.auc(valid=True)

stopping_rounds

Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable)

Type: int (default: 0).

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> airlines["Year"] = airlines["Year"].asfactor()
>>> airlines["Month"] = airlines["Month"].asfactor()
>>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
>>> airlines["Cancelled"] = airlines["Cancelled"].asfactor()
>>> airlines['FlightNum'] = airlines['FlightNum'].asfactor()
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> response = "IsDepDelayed"
>>> train, valid= airlines.split_frame(ratios=[.8],
...                                    seed=1234)
>>> airlines_drf = H2ORandomForestEstimator(stopping_metric="auc",
...                                         stopping_rounds=3,
...                                         stopping_tolerance=1e-2,
...                                         seed=1234)
>>> airlines_drf.train(x=predictors,
...                    y=response,
...                    training_frame=train,
...                    validation_frame=valid)
>>> airlines_drf.auc(valid=True)

stopping_tolerance

Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much)

Type: float (default: 0.001).

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> airlines["Year"] = airlines["Year"].asfactor()
>>> airlines["Month"] = airlines["Month"].asfactor()
>>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
>>> airlines["Cancelled"] = airlines["Cancelled"].asfactor()
>>> airlines['FlightNum'] = airlines['FlightNum'].asfactor()
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> response = "IsDepDelayed"
>>> train, valid= airlines.split_frame(ratios=[.8],
...                                    seed=1234)
>>> airlines_drf = H2ORandomForestEstimator(stopping_metric="auc",
...                                         stopping_rounds=3,
...                                         stopping_tolerance=1e-2,
...                                         seed=1234)
>>> airlines_drf.train(x=predictors,
...                    y=response,
...                    training_frame=train,
...                    validation_frame=valid)
>>> airlines_drf.auc(valid=True)

training_frame

Id of the training data frame.

Type: H2OFrame.

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> train, valid = cars.split_frame(ratios=[.8],
...                                 seed=1234)
>>> cars_drf = H2ORandomForestEstimator(seed=1234)
>>> cars_drf.train(x=predictors,
...                y=response,
...                training_frame=train,
...                validation_frame=valid)
>>> cars_drf.auc(valid=True)

validation_frame

Id of the validation data frame.

Type: H2OFrame.

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "economy_20mpg"
>>> train, valid = cars.split_frame(ratios=[.8],
...                                 seed=1234)
>>> cars_drf = H2ORandomForestEstimator(seed=1234)
>>> cars_drf.train(x=predictors,
...                y=response,
...                training_frame=train,
...                validation_frame=valid)
>>> cars_drf.auc(valid=True)

weights_column

Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor.

Type: str.

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","acceleration","year"]
>>> response = "economy_20mpg"
>>> train, valid = cars.split_frame(ratios=[.8],
...                                 seed=1234)
>>> cars_drf = H2ORandomForestEstimator(seed=1234)
>>> cars_drf.train(x=predictors,
...                y=response,
...                training_frame=train,
...                validation_frame=valid,
...                weights_column="weight")
>>> cars_drf.auc(valid=True)

H2OStackedEnsembleEstimator

class h2o.estimators.stackedensemble.H2OStackedEnsembleEstimator(**kwargs)

Bases: h2o.estimators.estimator_base.H2OEstimator

Stacked Ensemble

Builds a stacked ensemble (aka “super learner”) machine learning method that uses two or more H2O learning algorithms to improve predictive performance. It is a loss-based supervised learning method that finds the optimal combination of a collection of prediction algorithms.This method supports regression and binary classification.

Examples:

>>> import h2o
>>> h2o.init()
>>> from h2o.estimators.random_forest import H2ORandomForestEstimator
>>> from h2o.estimators.gbm import H2OGradientBoostingEstimator
>>> from h2o.estimators.stackedensemble import H2OStackedEnsembleEstimator
>>> col_types = ["numeric", "numeric", "numeric", "enum",
...              "enum", "numeric", "numeric", "numeric", "numeric"]
>>> data = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/prostate/prostate.csv", col_types=col_types)
>>> train, test = data.split_frame(ratios=[.8], seed=1)
>>> x = ["CAPSULE","GLEASON","RACE","DPROS","DCAPS","PSA","VOL"]
>>> y = "AGE"
>>> nfolds = 5
>>> gbm = H2OGradientBoostingEstimator(nfolds=nfolds,
...                                    fold_assignment="Modulo",
...                                    keep_cross_validation_predictions=True)
>>> gbm.train(x=x, y=y, training_frame=train)
>>> rf = H2ORandomForestEstimator(nfolds=nfolds,
...                               fold_assignment="Modulo",
...                               keep_cross_validation_predictions=True)
>>> rf.train(x=x, y=y, training_frame=train)
>>> stack = H2OStackedEnsembleEstimator(model_id="ensemble",
...                                     training_frame=train,
...                                     validation_frame=test,
...                                     base_models=[gbm.model_id, rf.model_id])
>>> stack.train(x=x, y=y, training_frame=train, validation_frame=test)
>>> stack.model_performance()

base_models

List of models or grids (or their ids) to ensemble/stack together. Grids are expanded to individual models. If not using blending frame, then models must have been cross-validated using nfolds > 1, and folds must be identical across models.

Type: List[str] (default: []).

Examples:

>>> from h2o.estimators.random_forest import H2ORandomForestEstimator
>>> from h2o.estimators.gbm import H2OGradientBoostingEstimator
>>> from h2o.estimators.stackedensemble import H2OStackedEnsembleEstimator
>>> col_types = ["numeric", "numeric", "numeric", "enum",
...              "enum", "numeric", "numeric", "numeric", "numeric"]
>>> data = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/prostate/prostate.csv", col_types=col_types)
>>> train, test = data.split_frame(ratios=[.8], seed=1)
>>> x = ["CAPSULE","GLEASON","RACE","DPROS","DCAPS","PSA","VOL"]
>>> y = "AGE"
>>> nfolds = 5
>>> gbm = H2OGradientBoostingEstimator(nfolds=nfolds,
...                                    fold_assignment="Modulo",
...                                    keep_cross_validation_predictions=True)
>>> gbm.train(x=x, y=y, training_frame=train)
>>> rf = H2ORandomForestEstimator(nfolds=nfolds,
...                               fold_assignment="Modulo",
...                               keep_cross_validation_predictions=True)
>>> rf.train(x=x, y=y, training_frame=train)
>>> stack = H2OStackedEnsembleEstimator(model_id="ensemble",
...                                     training_frame=train,
...                                     validation_frame=test,
...                                     base_models=[gbm.model_id, rf.model_id])
>>> stack.train(x=x, y=y, training_frame=train, validation_frame=test)
>>> stack.model_performance()

blending_frame

Frame used to compute the predictions that serve as the training frame for the metalearner (triggers blending mode if provided)

Type: H2OFrame.

Examples:

>>> from h2o.estimators.random_forest import H2ORandomForestEstimator
>>> from h2o.estimators.gbm import H2OGradientBoostingEstimator
>>> from h2o.estimators.stackedensemble import H2OStackedEnsembleEstimator
>>> higgs = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/higgs_train_5k.csv")
>>> train, blend = higgs.split_frame(ratios = [.8], seed = 1234)
>>> x = train.columns
>>> y = "response"
>>> x.remove(y)
>>> train[y] = train[y].asfactor()
>>> blend[y] = blend[y].asfactor()
>>> nfolds = 3
>>> my_gbm = H2OGradientBoostingEstimator(distribution="bernoulli",
...                                       ntrees=10,
...                                       nfolds=nfolds,
...                                       fold_assignment="Modulo",
...                                       keep_cross_validation_predictions=True,
...                                       seed=1)
>>> my_gbm.train(x=x, y=y, training_frame=train)
>>> my_rf = H2ORandomForestEstimator(ntrees=50,
...                                  nfolds=nfolds,
...                                  fold_assignment="Modulo",
...                                  keep_cross_validation_predictions=True,
...                                  seed=1)
>>> my_rf.train(x=x, y=y, training_frame=train)
>>> stack_blend = H2OStackedEnsembleEstimator(base_models=[my_gbm, my_rf],
...                                           seed=1)
>>> stack_blend.train(x=x, y=y, training_frame=train, blending_frame=blend)
>>> stack_blend.model_performance(blend).auc()

export_checkpoints_dir

Automatically export generated models to this directory.

Type: str.

Examples:

>>> from h2o.estimators.random_forest import H2ORandomForestEstimator
>>> from h2o.estimators.gbm import H2OGradientBoostingEstimator
>>> from h2o.estimators.stackedensemble import H2OStackedEnsembleEstimator
>>> import tempfile
>>> from os import listdir
>>> higgs = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/higgs_train_5k.csv")
>>> train, blend = higgs.split_frame(ratios = [.8], seed = 1234)
>>> x = train.columns
>>> y = "response"
>>> x.remove(y)
>>> train[y] = train[y].asfactor()
>>> blend[y] = blend[y].asfactor()
>>> nfolds = 3
>>> checkpoints_dir = tempfile.mkdtemp()
>>> my_gbm = H2OGradientBoostingEstimator(distribution="bernoulli",
...                                       ntrees=10,
...                                       nfolds=nfolds,
...                                       fold_assignment="Modulo",
...                                       keep_cross_validation_predictions=True,
...                                       seed=1)
>>> my_gbm.train(x=x, y=y, training_frame=train)
>>> my_rf = H2ORandomForestEstimator(ntrees=50,
...                                  nfolds=nfolds,
...                                  fold_assignment="Modulo",
...                                  keep_cross_validation_predictions=True,
...                                  seed=1)
>>> my_rf.train(x=x, y=y, training_frame=train)
>>> stack_blend = H2OStackedEnsembleEstimator(base_models=[my_gbm, my_rf],
...                                           seed=1,
...                                           export_checkpoints_dir=checkpoints_dir)
>>> stack_blend.train(x=x, y=y, training_frame=train, blending_frame=blend)
>>> len(listdir(checkpoints_dir))

keep_levelone_frame

Keep level one frame used for metalearner training.

Type: bool (default: False).

Examples:

>>> from h2o.estimators.random_forest import H2ORandomForestEstimator
>>> from h2o.estimators.gbm import H2OGradientBoostingEstimator
>>> from h2o.estimators.stackedensemble import H2OStackedEnsembleEstimator
>>> higgs = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/higgs_train_5k.csv")
>>> train, blend = higgs.split_frame(ratios = [.8], seed = 1234)
>>> x = train.columns
>>> y = "response"
>>> x.remove(y)
>>> train[y] = train[y].asfactor()
>>> blend[y] = blend[y].asfactor()
>>> nfolds = 3
>>> my_gbm = H2OGradientBoostingEstimator(distribution="bernoulli",
...                                       ntrees=1,
...                                       nfolds=nfolds,
...                                       fold_assignment="Modulo",
...                                       keep_cross_validation_predictions=True,
...                                       seed=1)
>>> my_gbm.train(x=x, y=y, training_frame=train)
>>> my_rf = H2ORandomForestEstimator(ntrees=50,
...                                  nfolds=nfolds,
...                                  fold_assignment="Modulo",
...                                  keep_cross_validation_predictions=True,
...                                  seed=1)
>>> my_rf.train(x=x, y=y, training_frame=train)
>>> stack_blend = H2OStackedEnsembleEstimator(base_models=[my_gbm, my_rf],
...                                           seed=1,
...                                           keep_levelone_frame=True)
>>> stack_blend.train(x=x, y=y, training_frame=train, blending_frame=blend)
>>> stack_blend.model_performance(blend).auc()

levelone_frame_id()

Fetch the levelone_frame_id for an H2OStackedEnsembleEstimator.

Examples:

>>> from h2o.estimators.random_forest import H2ORandomForestEstimator
>>> from h2o.estimators.gbm import H2OGradientBoostingEstimator
>>> from h2o.estimators.stackedensemble import H2OStackedEnsembleEstimator
>>> higgs = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/higgs_train_5k.csv")
>>> train, blend = higgs.split_frame(ratios = [.8], seed = 1234)
>>> x = train.columns
>>> y = "response"
>>> x.remove(y)
>>> train[y] = train[y].asfactor()
>>> blend[y] = blend[y].asfactor()
>>> nfolds = 3
>>> my_gbm = H2OGradientBoostingEstimator(distribution="bernoulli",
...                                       ntrees=10,
...                                       nfolds=nfolds,
...                                       fold_assignment="Modulo",
...                                       keep_cross_validation_predictions=True,
...                                       seed=1)
>>> my_gbm.train(x=x, y=y, training_frame=train)
>>> my_rf = H2ORandomForestEstimator(ntrees=50,
...                                  nfolds=nfolds,
...                                  fold_assignment="Modulo",
...                                  keep_cross_validation_predictions=True,
...                                  seed=1)
>>> my_rf.train(x=x, y=y, training_frame=train)
>>> stack_blend = H2OStackedEnsembleEstimator(base_models=[my_gbm, my_rf],
...                                           seed=1,
...                                           keep_levelone_frame=True)
>>> stack_blend.train(x=x, y=y, training_frame=train, blending_frame=blend)
>>> stack_blend.levelone_frame_id()

metalearner()

Print the metalearner of an H2OStackedEnsembleEstimator.

Examples:

>>> from h2o.estimators.random_forest import H2ORandomForestEstimator
>>> from h2o.estimators.gbm import H2OGradientBoostingEstimator
>>> from h2o.estimators.stackedensemble import H2OStackedEnsembleEstimator
>>> higgs = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/higgs_train_5k.csv")
>>> train, blend = higgs.split_frame(ratios = [.8], seed = 1234)
>>> x = train.columns
>>> y = "response"
>>> x.remove(y)
>>> train[y] = train[y].asfactor()
>>> blend[y] = blend[y].asfactor()
>>> nfolds = 3
>>> my_gbm = H2OGradientBoostingEstimator(distribution="bernoulli",
...                                       ntrees=10,
...                                       nfolds=nfolds,
...                                       fold_assignment="Modulo",
...                                       keep_cross_validation_predictions=True,
...                                       seed=1)
>>> my_gbm.train(x=x, y=y, training_frame=train)
>>> my_rf = H2ORandomForestEstimator(ntrees=50,
...                                  nfolds=nfolds,
...                                  fold_assignment="Modulo",
...                                  keep_cross_validation_predictions=True,
...                                  seed=1)
>>> my_rf.train(x=x, y=y, training_frame=train)
>>> stack_blend = H2OStackedEnsembleEstimator(base_models=[my_gbm, my_rf],
...                                           seed=1,
...                                           keep_levelone_frame=True)
>>> stack_blend.train(x=x, y=y, training_frame=train, blending_frame=blend)
>>> stack_blend.metalearner

metalearner_algorithm

Type of algorithm to use as the metalearner. Options include ‘AUTO’ (GLM with non negative weights; if validation_frame is present, a lambda search is performed), ‘deeplearning’ (Deep Learning with default parameters), ‘drf’ (Random Forest with default parameters), ‘gbm’ (GBM with default parameters), ‘glm’ (GLM with default parameters), ‘naivebayes’ (NaiveBayes with default parameters), or ‘xgboost’ (if available, XGBoost with default parameters).

One of: "auto", "deeplearning", "drf", "gbm", "glm", "naivebayes", "xgboost" (default: "auto").

Examples:

>>> from h2o.estimators.random_forest import H2ORandomForestEstimator
>>> from h2o.estimators.gbm import H2OGradientBoostingEstimator
>>> from h2o.estimators.stackedensemble import H2OStackedEnsembleEstimator
>>> higgs = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/higgs_train_5k.csv")
>>> train, blend = higgs.split_frame(ratios = [.8], seed = 1234)
>>> x = train.columns
>>> y = "response"
>>> x.remove(y)
>>> train[y] = train[y].asfactor()
>>> blend[y] = blend[y].asfactor()
>>> nfolds = 3
>>> my_gbm = H2OGradientBoostingEstimator(distribution="bernoulli",
...                                       ntrees=1,
...                                       nfolds=nfolds,
...                                       fold_assignment="Modulo",
...                                       keep_cross_validation_predictions=True,
...                                       seed=1)
>>> my_gbm.train(x=x, y=y, training_frame=train)
>>> my_rf = H2ORandomForestEstimator(ntrees=50,
...                                  nfolds=nfolds,
...                                  fold_assignment="Modulo",
...                                  keep_cross_validation_predictions=True,
...                                  seed=1)
>>> my_rf.train(x=x, y=y, training_frame=train)
>>> stack_blend = H2OStackedEnsembleEstimator(base_models=[my_gbm, my_rf],
...                                           seed=1,
...                                           metalearner_algorithm="gbm")
>>> stack_blend.train(x=x, y=y, training_frame=train, blending_frame=blend)
>>> stack_blend.model_performance(blend).auc()

metalearner_fold_assignment

Cross-validation fold assignment scheme for metalearner cross-validation. Defaults to AUTO (which is currently set to Random). The ‘Stratified’ option will stratify the folds based on the response variable, for classification problems.

One of: "auto", "random", "modulo", "stratified".

Examples:

>>> from h2o.estimators.random_forest import H2ORandomForestEstimator
>>> from h2o.estimators.gbm import H2OGradientBoostingEstimator
>>> from h2o.estimators.stackedensemble import H2OStackedEnsembleEstimator
>>> higgs = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/higgs_train_5k.csv")
>>> train, blend = higgs.split_frame(ratios = [.8], seed = 1234)
>>> x = train.columns
>>> y = "response"
>>> x.remove(y)
>>> train[y] = train[y].asfactor()
>>> blend[y] = blend[y].asfactor()
>>> nfolds = 3
>>> my_gbm = H2OGradientBoostingEstimator(distribution="bernoulli",
...                                       ntrees=1,
...                                       nfolds=nfolds,
...                                       fold_assignment="Modulo",
...                                       keep_cross_validation_predictions=True,
...                                       seed=1)
>>> my_gbm.train(x=x, y=y, training_frame=train)
>>> my_rf = H2ORandomForestEstimator(ntrees=50,
...                                  nfolds=nfolds,
...                                  fold_assignment="Modulo",
...                                  keep_cross_validation_predictions=True,
...                                  seed=1)
>>> my_rf.train(x=x, y=y, training_frame=train)
>>> stack_blend = H2OStackedEnsembleEstimator(base_models=[my_gbm, my_rf],
...                                           seed=1,
...                                           metalearner_fold_assignment="Random")
>>> stack_blend.train(x=x, y=y, training_frame=train, blending_frame=blend)
>>> stack_blend.model_performance(blend).auc()

metalearner_fold_column

Column with cross-validation fold index assignment per observation for cross-validation of the metalearner.

Type: str.

Examples:

>>> from h2o.estimators.random_forest import H2ORandomForestEstimator
>>> from h2o.estimators.gbm import H2OGradientBoostingEstimator
>>> from h2o.estimators.stackedensemble import H2OStackedEnsembleEstimator
>>> train = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/higgs_train_5k.csv")
>>> test = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/higgs_test_5k.csv")
>>> fold_column = "fold_id"
>>> train[fold_column] = train.kfold_column(n_folds=3, seed=1)
>>> x = train.columns
>>> y = "response"
>>> x.remove(y)
>>> x.remove(fold_column)
>>> train[y] = train[y].asfactor()
>>> test[y] = test[y].asfactor()
>>> nfolds = 3
>>> my_gbm = H2OGradientBoostingEstimator(distribution="bernoulli",
...                                       ntrees=10,
...                                       nfolds=nfolds,
...                                       fold_assignment="Modulo",
...                                       keep_cross_validation_predictions=True,
...                                       seed=1)
>>> my_gbm.train(x=x, y=y, training_frame=train)
>>> my_rf = H2ORandomForestEstimator(ntrees=50,
...                                  nfolds=nfolds,
...                                  fold_assignment="Modulo",
...                                  keep_cross_validation_predictions=True,
...                                  seed=1)
>>> my_rf.train(x=x, y=y, training_frame=train)
>>> stack = H2OStackedEnsembleEstimator(base_models=[my_gbm, my_rf],
...                                     metalearner_fold_column=fold_column,
...                                     metalearner_params=dict(keep_cross_validation_models=True))
>>> stack.train(x=x, y=y, training_frame=train)
>>> stack.model_performance().auc()

metalearner_nfolds

Number of folds for K-fold cross-validation of the metalearner algorithm (0 to disable or >= 2).

Type: int (default: 0).

Examples:

>>> from h2o.estimators.random_forest import H2ORandomForestEstimator
>>> from h2o.estimators.gbm import H2OGradientBoostingEstimator
>>> from h2o.estimators.stackedensemble import H2OStackedEnsembleEstimator
>>> higgs = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/higgs_train_5k.csv")
>>> train, blend = higgs.split_frame(ratios = [.8], seed = 1234)
>>> x = train.columns
>>> y = "response"
>>> x.remove(y)
>>> train[y] = train[y].asfactor()
>>> blend[y] = blend[y].asfactor()
>>> nfolds = 3
>>> my_gbm = H2OGradientBoostingEstimator(distribution="bernoulli",
...                                       ntrees=1,
...                                       nfolds=nfolds,
...                                       fold_assignment="Modulo",
...                                       keep_cross_validation_predictions=True,
...                                       seed=1)
>>> my_gbm.train(x=x, y=y, training_frame=train)
>>> my_rf = H2ORandomForestEstimator(ntrees=50,
...                                  nfolds=nfolds,
...                                  fold_assignment="Modulo",
...                                  keep_cross_validation_predictions=True,
...                                  seed=1)
>>> my_rf.train(x=x, y=y, training_frame=train)
>>> stack_blend = H2OStackedEnsembleEstimator(base_models=[my_gbm, my_rf],
...                                           seed=1,
...                                           metalearner_nfolds=3)
>>> stack_blend.train(x=x, y=y, training_frame=train, blending_frame=blend)
>>> stack_blend.model_performance(blend).auc()

metalearner_params

Parameters for metalearner algorithm

Type: dict.

Examples:

>>> from h2o.estimators.random_forest import H2ORandomForestEstimator
>>> from h2o.estimators.gbm import H2OGradientBoostingEstimator
>>> from h2o.estimators.stackedensemble import H2OStackedEnsembleEstimator
>>> higgs = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/higgs_train_5k.csv")
>>> train, blend = higgs.split_frame(ratios = [.8], seed = 1234)
>>> x = train.columns
>>> y = "response"
>>> x.remove(y)
>>> train[y] = train[y].asfactor()
>>> blend[y] = blend[y].asfactor()
>>> nfolds = 3
>>> gbm_params = {"ntrees" : 100, "max_depth" : 6}
>>> my_gbm = H2OGradientBoostingEstimator(distribution="bernoulli",
...                                       ntrees=1,
...                                       nfolds=nfolds,
...                                       fold_assignment="Modulo",
...                                       keep_cross_validation_predictions=True,
...                                       seed=1)
>>> my_gbm.train(x=x, y=y, training_frame=train)
>>> my_rf = H2ORandomForestEstimator(ntrees=50,
...                                  nfolds=nfolds,
...                                  fold_assignment="Modulo",
...                                  keep_cross_validation_predictions=True,
...                                  seed=1)
>>> my_rf.train(x=x, y=y, training_frame=train)
>>> stack_blend = H2OStackedEnsembleEstimator(base_models=[my_gbm, my_rf],
...                                           metalearner_algorithm="gbm",
...                                           metalearner_params=gbm_params)
>>> stack_blend.train(x=x, y=y, training_frame=train, blending_frame=blend)
>>> stack_blend.model_performance(blend).auc()

response_column

Response variable column.

Type: str.

seed

Seed for random numbers; passed through to the metalearner algorithm. Defaults to -1 (time-based random number)

Type: int (default: -1).

Examples:

>>> from h2o.estimators.random_forest import H2ORandomForestEstimator
>>> from h2o.estimators.gbm import H2OGradientBoostingEstimator
>>> from h2o.estimators.stackedensemble import H2OStackedEnsembleEstimator
>>> higgs = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/higgs_train_5k.csv")
>>> train, blend = higgs.split_frame(ratios = [.8], seed = 1234)
>>> x = train.columns
>>> y = "response"
>>> x.remove(y)
>>> train[y] = train[y].asfactor()
>>> blend[y] = blend[y].asfactor()
>>> nfolds = 3
>>> my_gbm = H2OGradientBoostingEstimator(distribution="bernoulli",
...                                       ntrees=1,
...                                       nfolds=nfolds,
...                                       fold_assignment="Modulo",
...                                       keep_cross_validation_predictions=True,
...                                       seed=1)
>>> my_gbm.train(x=x, y=y, training_frame=train)
>>> my_rf = H2ORandomForestEstimator(ntrees=50,
...                                  nfolds=nfolds,
...                                  fold_assignment="Modulo",
...                                  keep_cross_validation_predictions=True,
...                                  seed=1)
>>> my_rf.train(x=x, y=y, training_frame=train)
>>> stack_blend = H2OStackedEnsembleEstimator(base_models=[my_gbm, my_rf],
...                                           seed=1,
...                                           metalearner_fold_assignment="Random")
>>> stack_blend.train(x=x, y=y, training_frame=train, blending_frame=blend)
>>> stack_blend.model_performance(blend).auc()

training_frame

Id of the training data frame.

Type: H2OFrame.

Examples:

>>> from h2o.estimators.random_forest import H2ORandomForestEstimator
>>> from h2o.estimators.gbm import H2OGradientBoostingEstimator
>>> from h2o.estimators.stackedensemble import H2OStackedEnsembleEstimator
>>> higgs = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/higgs_train_5k.csv")
>>> train, valid = higgs.split_frame(ratios = [.8], seed = 1234)
>>> x = train.columns
>>> y = "response"
>>> x.remove(y)
>>> train[y] = train[y].asfactor()
>>> blend[y] = blend[y].asfactor()
>>> nfolds = 3
>>> my_gbm = H2OGradientBoostingEstimator(distribution="bernoulli",
...                                       ntrees=1,
...                                       nfolds=nfolds,
...                                       fold_assignment="Modulo",
...                                       keep_cross_validation_predictions=True,
...                                       seed=1)
>>> my_gbm.train(x=x, y=y, training_frame=train)
>>> my_rf = H2ORandomForestEstimator(ntrees=50,
...                                  nfolds=nfolds,
...                                  fold_assignment="Modulo",
...                                  keep_cross_validation_predictions=True,
...                                  seed=1)
>>> my_rf.train(x=x, y=y, training_frame=train)
>>> stack_blend = H2OStackedEnsembleEstimator(base_models=[my_gbm, my_rf],
...                                           seed=1,
...                                           metalearner_fold_assignment="Random")
>>> stack_blend.train(x=x, y=y, training_frame=train, validation_frame=valid)
>>> stack_blend.model_performance(blend).auc()

validation_frame

Id of the validation data frame.

Type: H2OFrame.

Examples:

>>> from h2o.estimators.random_forest import H2ORandomForestEstimator
>>> from h2o.estimators.gbm import H2OGradientBoostingEstimator
>>> from h2o.estimators.stackedensemble import H2OStackedEnsembleEstimator
>>> higgs = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/higgs_train_5k.csv")
>>> train, valid = higgs.split_frame(ratios = [.8], seed = 1234)
>>> x = train.columns
>>> y = "response"
>>> x.remove(y)
>>> train[y] = train[y].asfactor()
>>> blend[y] = blend[y].asfactor()
>>> nfolds = 3 
>>> my_gbm = H2OGradientBoostingEstimator(distribution="bernoulli",
...                                       ntrees=1,
...                                       nfolds=nfolds,
...                                       fold_assignment="Modulo",
...                                       keep_cross_validation_predictions=True,
...                                       seed=1)
>>> my_gbm.train(x=x, y=y, training_frame=train)
>>> my_rf = H2ORandomForestEstimator(ntrees=50,
...                                  nfolds=nfolds,
...                                  fold_assignment="Modulo",
...                                  keep_cross_validation_predictions=True,
...                                  seed=1)
>>> my_rf.train(x=x, y=y, training_frame=train)
>>> stack_blend = H2OStackedEnsembleEstimator(base_models=[my_gbm, my_rf],
...                                           seed=1,
...                                           metalearner_fold_assignment="Random")
>>> stack_blend.train(x=x, y=y, training_frame=train, validation_frame=valid)
>>> stack_blend.model_performance(blend).auc()

H2OTargetEncoderEstimator

class h2o.estimators.targetencoder.H2OTargetEncoderEstimator(**kwargs)

Bases: h2o.estimators.estimator_base.H2OEstimator

TargetEncoder

blending

Blending enabled/disabled

Type: bool (default: False).

Examples:

>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv")
>>> predictors = ["home.dest", "cabin", "embarked"]
>>> response = "survived"
>>> titanic["survived"] = titanic["survived"].asfactor()
>>> fold_col = "kfold_column"
>>> titanic[fold_col] = titanic.kfold_column(n_folds=5, seed=1234)
>>> titanic_te = H2OTargetEncoderEstimator(k=35,
...                                        f=25,
...                                        blending=True)
>>> titanic_te.train(x=predictors,
...                  y=response,
...                  training_frame=titanic)
>>> titanic_te

data_leakage_handling

Data leakage handling strategy.

One of: "none", "k_fold", "leave_one_out" (default: "none").

Examples:

>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv")
>>> predictors = ["home.dest", "cabin", "embarked"]
>>> response = "survived"
>>> titanic["survived"] = titanic["survived"].asfactor()
>>> fold_col = "kfold_column"
>>> titanic[fold_col] = titanic.kfold_column(n_folds=5, seed=1234)
>>> titanic_te = H2OTargetEncoderEstimator(k=35,
...                                        f=25,
...                                        data_leakage_handling="k_fold",
...                                        blending=True)
>>> titanic_te.train(x=predictors,
...                  y=response,
...                  training_frame=titanic)
>>> titanic_te

f

Smoothing. Used for blending (if enabled). Blending is to be enabled separately using the ‘blending’ parameter.

Type: float (default: 20).

Examples:

>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv")
>>> predictors = ["home.dest", "cabin", "embarked"]
>>> response = "survived"
>>> titanic["survived"] = titanic["survived"].asfactor()
>>> fold_col = "kfold_column"
>>> titanic[fold_col] = titanic.kfold_column(n_folds=5, seed=1234)
>>> titanic_te = H2OTargetEncoderEstimator(k=35,
...                                        f=25,
...                                        blending=True)
>>> titanic_te.train(x=predictors,
...                  y=response,
...                  training_frame=titanic)
>>> titanic_te

fold_column

Column with cross-validation fold index assignment per observation.

Type: str.

Examples:

>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv")
>>> predictors = ["home.dest", "cabin", "embarked"]
>>> response = "survived"
>>> titanic["survived"] = titanic["survived"].asfactor()
>>> fold_col = "kfold_column"
>>> titanic[fold_col] = titanic.kfold_column(n_folds=5, seed=1234)
>>> titanic_te = H2OTargetEncoderEstimator(k=35,
...                                        f=25,
...                                        blending=True)
>>> titanic_te.train(x=predictors,
...                  y=response,
...                  training_frame=titanic)
>>> titanic_te

ignored_columns

Names of columns to ignore for training.

Type: List[str].

k

Inflection point. Used for blending (if enabled). Blending is to be enabled separately using the ‘blending’ parameter.

Type: float (default: 10).

Examples:

>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv")
>>> predictors = ["home.dest", "cabin", "embarked"]
>>> response = "survived"
>>> titanic["survived"] = titanic["survived"].asfactor()
>>> fold_col = "kfold_column"
>>> titanic[fold_col] = titanic.kfold_column(n_folds=5, seed=1234)
>>> titanic_te = H2OTargetEncoderEstimator(k=35,
...                                        f=25,
...                                        blending=True)
>>> titanic_te.train(x=predictors,
...                  y=response,
...                  training_frame=titanic)
>>> titanic_te

noise_level

Noise level

Type: float (default: 0.01).

response_column

Response variable column.

Type: str.

seed

Seed for the specified noise level

Type: int (default: -1).

training_frame

Id of the training data frame.

Type: H2OFrame.

Examples:

>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv")
>>> predictors = ["home.dest", "cabin", "embarked"]
>>> response = "survived"
>>> titanic["survived"] = titanic["survived"].asfactor()
>>> fold_col = "kfold_column"
>>> titanic[fold_col] = titanic.kfold_column(n_folds=5, seed=1234)
>>> titanic_te = H2OTargetEncoderEstimator(k=35,
...                                        f=25,
...                                        blending=True)
>>> titanic_te.train(x=predictors,
...                  y=response,
...                  training_frame=titanic)
>>> titanic_te

transform(frame, data_leakage_handling='None', noise=-1, seed=-1)

Apply transformation to te_columns based on the encoding maps generated during train() method call.

Parameters:

• frame (H2OFrame) – to which frame we are applying target encoding transformations.
• data_leakage_handling (str) – Supported options:

1. “k_fold” - encodings for a fold are generated based on out-of-fold data.
2. “leave_one_out” - leave one out. Current row’s response value is subtracted from the pre-calculated per-level frequencies.
3. “none” - we do not holdout anything. Using whole frame for training

Parameters:

• noise (float) – the amount of random noise added to the target encoding. This helps prevent overfitting. Defaults to 0.01 * range of y.
• seed (int) – a random seed used to generate draws from the uniform distribution for random noise. Defaults to -1.

Example:

>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv")
>>> predictors = ["home.dest", "cabin", "embarked"]
>>> response = "survived"
>>> titanic[response] = titanic[response].asfactor()
>>> fold_col = "kfold_column"
>>> titanic[fold_col] = titanic.kfold_column(n_folds=5, seed=1234)
>>> titanic_te = H2OTargetEncoderEstimator(k=35,
...                                        f=25,
...                                        data_leakage_handling="leave_one_out",
...                                        blending=True)
>>> titanic_te.train(x=predictors,
...                  y=response,
...                  training_frame=titanic)
>>> transformed = titanic_te.transform(frame=titanic,
...                                    data_leakage_handling="leave_one_out",
...                                    seed=1234)

H2OXGBoostEstimator

class h2o.estimators.xgboost.H2OXGBoostEstimator(**kwargs)

Bases: h2o.estimators.estimator_base.H2OEstimator

XGBoost

Builds an eXtreme Gradient Boosting model using the native XGBoost backend.

static available()

Ask the H2O server whether a XGBoost model can be built (depends on availability of native backends). :return: True if a XGBoost model can be built, or False otherwise.

Examples:

>>> boston = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv")
>>> predictors = boston.columns[:-1]
>>> response = "medv"
>>> boston['chas'] = boston['chas'].asfactor()
>>> train, valid = boston.split_frame(ratios=[.8])
>>> boston_xgb = H2OXGBoostEstimator(seed=1234)
>>> boston_xgb.available()

backend

Backend. By default (auto), a GPU is used if available.

One of: "auto", "gpu", "cpu" (default: "auto").

Examples:

>>> pros = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv")
>>> pros["CAPSULE"] = pros["CAPSULE"].asfactor()
>>> pros_xgb = H2OXGBoostEstimator(tree_method="exact",
...                                seed=123,
...                                backend="cpu")
>>> pros_xgb.train(y="CAPSULE",
...                ignored_columns=["ID"],
...                training_frame=pros)
>>> pros_xgb.auc()

booster

Booster type

One of: "gbtree", "gblinear", "dart" (default: "gbtree").

Examples:

>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv")
>>> titanic['survived'] = titanic['survived'].asfactor()
>>> predictors = titanic.columns
>>> response = 'survived'
>>> train, valid = titanic.split_frame(ratios=[.8],
...                                    seed=1234)
>>> titanic_xgb = H2OXGBoostEstimator(booster='dart',
...                                   normalize_type="tree",
...                                   seed=1234)
>>> titanic_xgb.train(x=predictors,
...                   y=response,
...                   training_frame=train,
...                   validation_frame=valid)
>>> print(titanic_xgb.auc(valid=True))

build_tree_one_node

Run on one node only; no network overhead but fewer cpus used. Suitable for small datasets.

Type: bool (default: False).

calibrate_model

Use Platt Scaling to calculate calibrated class probabilities. Calibration can provide more accurate estimates of class probabilities.

Type: bool (default: False).

calibration_frame

Calibration frame for Platt Scaling

Type: H2OFrame.

categorical_encoding

Encoding scheme for categorical features

One of: "auto", "enum", "one_hot_internal", "one_hot_explicit", "binary", "eigen", "label_encoder", "sort_by_response", "enum_limited" (default: "auto").

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> airlines["Year"] = airlines["Year"].asfactor()
>>> airlines["Month"] = airlines["Month"].asfactor()
>>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
>>> airlines["Cancelled"] = airlines["Cancelled"].asfactor()
>>> airlines['FlightNum'] = airlines['FlightNum'].asfactor()
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> response = "IsDepDelayed"
>>> train, valid= airlines.split_frame(ratios=[.8],
...                                    seed=1234)
>>> encoding = "one_hot_explicit"
>>> airlines_xgb = H2OXGBoostEstimator(categorical_encoding=encoding,
...                                    seed=1234)
>>> airlines_xgb.train(x=predictors,
...                    y=response,
...                    training_frame=train,
...                    validation_frame=valid)
>>> airlines_xgb.auc(valid=True)

checkpoint

Model checkpoint to resume training with.

Type: str.

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
>>> predictors = ["displacement","power","weight","year","economy_20mpg"]
>>> response = "acceleration"
>>> from h2o.estimators import H2OXGBoostEstimator
>>> cars_xgb = H2OXGBoostEstimator(seed=1234)
>>> train, valid = cars.split_frame(ratios=[.8])
>>> cars_xgb.train(x=predictors,
...                y=response,
...                training_frame=train,
...                validation_frame=valid)
>>> cars_xgb.mse()
>>> cars_xgb_continued = H2OXGBoostEstimator(checkpoint=cars_xgb.model_id,
...                                          ntrees=51,
...                                          seed=1234)
>>> cars_xgb_continued.train(x=predictors,
...                          y=response,
...                          training_frame=train,
...                          validation_frame=valid)
>>> cars_xgb_continued.mse()

col_sample_rate

(same as colsample_bylevel) Column sample rate (from 0.0 to 1.0)

Type: float (default: 1).

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> airlines["Year"] = airlines["Year"].asfactor()
>>> airlines["Month"] = airlines["Month"].asfactor()
>>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
>>> airlines["Cancelled"] = airlines["Cancelled"].asfactor()
>>> airlines['FlightNum'] = airlines['FlightNum'].asfactor()
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> response = "IsDepDelayed"
>>> train, valid= airlines.split_frame(ratios=[.8],
...                                    seed=1234)
>>> airlines_xgb = H2OXGBoostEstimator(col_sample_rate=.7,
...                                    seed=1234)
>>> airlines_xgb.train(x=predictors,
...                    y=response,
...                    training_frame=train,
...                    validation_frame=valid)
>>> print(airlines_xgb.auc(valid=True))

col_sample_rate_per_tree

(same as colsample_bytree) Column sample rate per tree (from 0.0 to 1.0)

Type: float (default: 1).

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> airlines["Year"] = airlines["Year"].asfactor()
>>> airlines["Month"] = airlines["Month"].asfactor()
>>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
>>> airlines["Cancelled"] = airlines["Cancelled"].asfactor()
>>> airlines['FlightNum'] = airlines['FlightNum'].asfactor()
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> response = "IsDepDelayed"
>>> train, valid= airlines.split_frame(ratios=[.8], seed=1234)
>>> airlines_xgb = H2OXGBoostEstimator(col_sample_rate_per_tree=.7,
...                                    seed=1234)
>>> airlines_xgb.train(x=predictors,
...                    y=response,
...                    training_frame=train,
...                    validation_frame=valid)
>>> print(airlines_xgb.auc(valid=True))

colsample_bylevel

(same as col_sample_rate) Column sample rate (from 0.0 to 1.0)

Type: float (default: 1).

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> airlines["Year"] = airlines["Year"].asfactor()
>>> airlines["Month"] = airlines["Month"].asfactor()
>>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
>>> airlines["Cancelled"] = airlines["Cancelled"].asfactor()
>>> airlines['FlightNum'] = airlines['FlightNum'].asfactor()
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> response = "IsDepDelayed"
>>> train, valid= airlines.split_frame(ratios=[.8],
...                                    seed=1234)
>>> airlines_xgb = H2OXGBoostEstimator(col_sample_rate=.7,
...                                    seed=1234)
>>> airlines_xgb.train(x=predictors,
...                    y=response,
...                    training_frame=train,
...                    validation_frame=valid)
>>> print(airlines_xgb.auc(valid=True))

colsample_bytree

(same as col_sample_rate_per_tree) Column sample rate per tree (from 0.0 to 1.0)

Type: float (default: 1).

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> airlines["Year"] = airlines["Year"].asfactor()
>>> airlines["Month"] = airlines["Month"].asfactor()
>>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
>>> airlines["Cancelled"] = airlines["Cancelled"].asfactor()
>>> airlines['FlightNum'] = airlines['FlightNum'].asfactor()
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> response = "IsDepDelayed"
>>> train, valid= airlines.split_frame(ratios=[.8], seed=1234)
>>> airlines_xgb = H2OXGBoostEstimator(col_sample_rate_per_tree=.7,
...                                    seed=1234)
>>> airlines_xgb.train(x=predictors,
...                    y=response,
...                    training_frame=train,
...                    validation_frame=valid)
>>> print(airlines_xgb.auc(valid=True))

distribution

Distribution function

One of: "auto", "bernoulli", "multinomial", "gaussian", "poisson", "gamma", "tweedie", "laplace", "quantile", "huber" (default: "auto").

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> response = "cylinders"
>>> train, valid = cars.split_frame(ratios=[.8],
...                                 seed=1234)
>>> cars_xgb = H2OXGBoostEstimator(distribution="poisson",
...                                seed=1234)
>>> cars_xgb.train(x=predictors,
...                y=response,
...                training_frame=train,
...                validation_frame=valid)
>>> cars_xgb.mse(valid=True)

dmatrix_type

Type of DMatrix. For sparse, NAs and 0 are treated equally.

One of: "auto", "dense", "sparse" (default: "auto").

Examples:

>>> boston = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv")
>>> predictors = boston.columns[:-1]
>>> response = "medv"
>>> boston['chas'] = boston['chas'].asfactor()
>>> train, valid = boston.split_frame(ratios=[.8])
>>> boston_xgb = H2OXGBoostEstimator(dmatrix_type="auto",
...                                  seed=1234)
>>> boston_xgb.train(x=predictors,
...                  y=response,
...                  training_frame=train,
...                  validation_frame=valid)
>>> boston_xgb.mse()

eta

(same as learn_rate) Learning rate (from 0.0 to 1.0)

Type: float (default: 0.3).

Examples:

>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv")
>>> titanic['survived'] = titanic['survived'].asfactor()
>>> predictors = titanic.columns
>>> del predictors[1:3]
>>> response = 'survived'
>>> train, valid = titanic.split_frame(ratios=[.8],
...                                    seed=1234)
>>> titanic_xgb = H2OXGBoostEstimator(ntrees=10000,
...                                   learn_rate=0.01,
...                                   stopping_rounds=5,
...                                   stopping_metric="AUC",
...                                   stopping_tolerance=1e-4,
...                                   seed=1234)
>>> titanic_xgb.train(x=predictors,
...                   y=response,
...                   training_frame=train,
...                   validation_frame=valid)
>>>  print(titanic_xgb.auc(valid=True))

export_checkpoints_dir

Automatically export generated models to this directory.

Type: str.

Examples:

>>> import tempfile
>>> from h2o.grid.grid_search import H2OGridSearch
>>> from os import listdir
>>> airlines = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip", destination_frame="air.hex")
>>> predictors = ["DayofMonth", "DayOfWeek"]
>>> response = "IsDepDelayed"
>>> hyper_parameters = {'ntrees': [5,10]}
>>> search_crit = {'strategy': "RandomDiscrete",
...                'max_models': 5,
...                'seed': 1234,
...                'stopping_rounds': 3,
...                'stopping_metric': "AUTO",
...                'stopping_tolerance': 1e-2}
>>> checkpoints_dir = tempfile.mkdtemp()
>>> air_grid = H2OGridSearch(H2OXGBoostEstimator,
...                          hyper_params=hyper_parameters,
...                          search_criteria=search_crit)
>>> air_grid.train(x=predictors,
...                y=response,
...                training_frame=airlines,
...                distribution="bernoulli",
...                learn_rate=0.1,
...                max_depth=3,
...                export_checkpoints_dir=checkpoints_dir)
>>> len(listdir(checkpoints_dir))

fold_assignment

Cross-validation fold assignment scheme, if fold_column is not specified. The ‘Stratified’ option will stratify the folds based on the response variable, for classification problems.

One of: "auto", "random", "modulo", "stratified" (default: "auto").

Examples:

>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv")
>>> titanic['survived'] = titanic['survived'].asfactor()
>>> predictors = titanic.columns
>>> response = 'survived'
>>> assignment_type = "Random"
>>> titanic_xgb = H2OXGBoostEstimator(fold_assignment=assignment_type,
...                                   nfolds=5,
...                                   seed=1234)
>>> titanic_xgb.train(x=predictors,
...                   y=response,
...                   training_frame=titanic)
>>> titanic_xgb.auc(xval=True)

fold_column

Column with cross-validation fold index assignment per observation.

Type: str.

Examples:

>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv")
>>> titanic['survived'] = titanic['survived'].asfactor()
>>> predictors = titanic.columns
>>> response = 'survived'
>>> fold_numbers = titanic.kfold_column(n_folds=5,
...                                     seed=1234)
>>> fold_numbers.set_names(["fold_numbers"])
>>> titanic = titanic.cbind(fold_numbers)
>>> print(titanic['fold_numbers'])
>>> titanic_xgb = H2OXGBoostEstimator(seed=1234)
>>> titanic_xgb.train(x=predictors,
...                   y=response,
...                   training_frame=titanic,
...                   fold_column="fold_numbers")
>>> titanic_xgb.auc(xval=True)

gamma

(same as min_split_improvement) Minimum relative improvement in squared error reduction for a split to happen

Type: float (default: 0).

Examples:

>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv")
>>> titanic['survived'] = titanic['survived'].asfactor()
>>> predictors = titanic.columns
>>> response = 'survived'
>>> train, valid = titanic.split_frame(ratios=[.8],
...                                    seed=1234)
>>> titanic_xgb = H2OXGBoostEstimator(min_split_improvement=1e-3,
...                                   seed=1234)
>>> titanic_xgb.train(x=predictors,
...                   y=response,
...                   training_frame=train,
...                   validation_frame=valid)
>>> print(titanic_xgb.auc(valid=True))

gpu_id

Which GPU to use.

Type: int (default: 0).

Examples:

>>> boston = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv")
>>> predictors = boston.columns[:-1]
>>> response = "medv"
>>> boston['chas'] = boston['chas'].asfactor()
>>> train, valid = boston.split_frame(ratios=[.8])
>>> boston_xgb = H2OXGBoostEstimator(gpu_id=0,
...                                  seed=1234)
>>> boston_xgb.train(x=predictors,
...                  y=response,
...                  training_frame=train,
...                  validation_frame=valid)
>>> boston_xgb.mse()

grow_policy

Grow policy - depthwise is standard GBM, lossguide is LightGBM

One of: "depthwise", "lossguide" (default: "depthwise").

Examples:

>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv")
>>> titanic['survived'] = titanic['survived'].asfactor()
>>> predictors = titanic.columns
>>> response = 'survived'
>>> titanic["const_1"] = 6
>>> titanic["const_2"] = 7
>>> train, valid = titanic.split_frame(ratios=[.8],
...                                    seed=1234)
>>> titanic_xgb = H2OXGBoostEstimator(seed=1234,
...                                   grow_policy="depthwise")
>>> titanic_xgb.train(x=predictors,
...                   y=response,
...                   training_frame=train,
...                   validation_frame=valid)
>>> titanic_xgb.auc(valid=True)

ignore_const_cols

Ignore constant columns.

Type: bool (default: True).

Examples:

>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv")
>>> titanic['survived'] = titanic['survived'].asfactor()
>>> predictors = titanic.columns
>>> response = 'survived'
>>> titanic["const_1"] = 6
>>> titanic["const_2"] = 7
>>> train, valid = titanic.split_frame(ratios=[.8],
...                                    seed=1234)
>>> titanic_xgb = H2OXGBoostEstimator(seed=1234,
...                                   ignore_const_cols=True)
>>> titanic_xgb.train(x=predictors,
...                   y=response,
...                   training_frame=train,
...                   validation_frame=valid)
>>> titanic_xgb.auc(valid=True)

ignored_columns

Names of columns to ignore for training.

Type: List[str].

keep_cross_validation_fold_assignment

Whether to keep the cross-validation fold assignment.

Type: bool (default: False).

Examples:

>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv")
>>> titanic['survived'] = titanic['survived'].asfactor()
>>> predictors = titanic.columns
>>> response = 'survived'
>>> train, valid = titanic.split_frame(ratios=[.8],
...                                    seed=1234)
>>> titanic_xgb = H2OXGBoostEstimator(keep_cross_validation_fold_assignment=True,
...                                   nfolds=5,
...                                   seed=1234)
>>> titanic_xgb.train(x=predictors,
...                   y=response,
...                   training_frame=train)
>>> titanic_xgb.cross_validation_fold_assignment()

keep_cross_validation_models

Whether to keep the cross-validation models.

Type: bool (default: True).

Examples:

>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv")
>>> titanic['survived'] = titanic['survived'].asfactor()
>>> predictors = titanic.columns
>>> response = 'survived'
>>> train, valid = titanic.split_frame(ratios=[.8],
...                                    seed=1234)
>>> titanic_xgb = H2OXGBoostEstimator(keep_cross_validation_models=True,
...                                   nfolds=5 ,
...                                   seed=1234)
>>> titanic_xgb.train(x=predictors,
...                   y=response,
...                   training_frame=train)
>>> titanic_xgb.cross_validation_models()

keep_cross_validation_predictions

Whether to keep the predictions of the cross-validation models.

Type: bool (default: False).

Examples:

>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv")
>>> titanic['survived'] = titanic['survived'].asfactor()
>>> predictors = titanic.columns
>>> response = 'survived'
>>> train, valid = titanic.split_frame(ratios=[.8],
...                                    seed=1234)
>>> titanic_xgb = H2OXGBoostEstimator(keep_cross_validation_predictions=True,
...                                   nfolds=5,
...                                   seed=1234)
>>> titanic_xgb.train(x=predictors,
...                   y=response,
...                   training_frame=train)
>>> titanic_xgb.cross_validation_predictions()

learn_rate

(same as eta) Learning rate (from 0.0 to 1.0)

Type: float (default: 0.3).

Examples:

>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv")
>>> titanic['survived'] = titanic['survived'].asfactor()
>>> predictors = titanic.columns
>>> del predictors[1:3]
>>> response = 'survived'
>>> train, valid = titanic.split_frame(ratios=[.8], seed=1234)
>>> titanic_xgb = H2OXGBoostEstimator(ntrees=10000,
...                                   learn_rate=0.01,
...                                   stopping_rounds=5,
...                                   stopping_metric="AUC",
...                                   stopping_tolerance=1e-4,
...                                   seed=1234)
>>> titanic_xgb.train(x=predictors,
...                   y=response,
...                   training_frame=train,
...                   validation_frame=valid)
>>> print(titanic_xgb.auc(valid=True))

max_abs_leafnode_pred

(same as max_delta_step) Maximum absolute value of a leaf node prediction

Type: float (default: 0).

Examples:

>>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data")
>>> covtype[54] = covtype[54].asfactor()
>>> predictors = covtype.columns[0:54]
>>> response = 'C55'
>>> train, valid = covtype.split_frame(ratios=[.8],
...                                    seed=1234)
>>> cov_xgb = H2OXGBoostEstimator(max_abs_leafnode_pred=float(2),
...                               seed=1234)
>>> cov_xgb.train(x=predictors,
...               y=response,
...               training_frame=train,
...               validation_frame=valid)
>>> print(cov_xgb.logloss(valid=True))

max_bins

For tree_method=hist only: maximum number of bins

Type: int (default: 256).

Examples:

>>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data")
>>> covtype[54] = covtype[54].asfactor()
>>> predictors = covtype.columns[0:54]
>>> response = 'C55'
>>> train, valid = covtype.split_frame(ratios=[.8],
...                                    seed=1234)
>>> cov_xgb = H2OXGBoostEstimator(max_bins=200,
...                               seed=1234)
>>> cov_xgb.train(x=predictors,
...               y=response,
...               training_frame=train,
...               validation_frame=valid)
>>> print(cov_xgb.logloss(valid=True))

max_delta_step

(same as max_abs_leafnode_pred) Maximum absolute value of a leaf node prediction

Type: float (default: 0).

Examples:

>>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data")
>>> covtype[54] = covtype[54].asfactor()
>>> predictors = covtype.columns[0:54]
>>> response = 'C55'
>>> train, valid = covtype.split_frame(ratios=[.8],
...                                    seed=1234)
>>> cov_xgb = H2OXGBoostEstimator(max_delta_step=float(2),
...                               seed=1234)
>>> cov_xgb.train(x=predictors,
...               y=response,
...               training_frame=train,
...               validation_frame=valid)
>>> print(cov_xgb.logloss(valid=True))

max_depth

Maximum tree depth.

Type: int (default: 6).

Examples:

>>> df = h2o.import_file(path = "http://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv")
>>> response = "survived"
>>> df[response] = df[response].asfactor()
>>> predictors = df.columns
>>> del predictors[1:3]
>>> train, valid, test = df.split_frame(ratios=[0.6,0.2],
...                                     seed=1234,
...                                     destination_frames=
...                                     ['train.hex',
...                                     'valid.hex',
...                                     'test.hex'])
>>> xgb = H2OXGBoostEstimator()
>>> xgb.train(x=predictors,
...           y=response,
...           training_frame=train)
>>> perf = xgb.model_performance(valid)
>>> print perf.auc()

max_leaves

For tree_method=hist only: maximum number of leaves

Type: int (default: 0).

Examples:

>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv")
>>> titanic['survived'] = titanic['survived'].asfactor()
>>> predictors = titanic.columns
>>> del predictors[1:3]
>>> response = 'survived'
>>> train, valid = titanic.split_frame(ratios=[.8],
...                                    seed=1234)
>>> titanic_xgb = H2OXGBoostEstimator(max_leaves=0, seed=1234)
>>> titanic_xgb.train(x=predictors,
...                   y=response,
...                   training_frame=train,
...                   validation_frame=valid)
>>> print(titanic_xgb.auc(valid=True))

max_runtime_secs

Maximum allowed runtime in seconds for model training. Use 0 to disable.

Type: float (default: 0).

Examples:

>>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data")
>>> covtype[54] = covtype[54].asfactor()
>>> predictors = covtype.columns[0:54]
>>> response = 'C55'
>>> train, valid = covtype.split_frame(ratios=[.8],
...                                    seed=1234)
>>> cov_xgb = H2OXGBoostEstimator(max_runtime_secs=10,
...                               ntrees=10000,
...                               max_depth=10,
...                               seed=1234)
>>> cov_xgb.train(x=predictors,
...               y=response,
...               training_frame=train,
...               validation_frame=valid)
>>> print(cov_xgb.logloss(valid=True))

min_child_weight

(same as min_rows) Fewest allowed (weighted) observations in a leaf.

Type: float (default: 1).

Examples:

>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv")
>>> titanic['survived'] = titanic['survived'].asfactor()
>>> predictors = titanic.columns
>>> del predictors[1:3]
>>> response = 'survived'
>>> train, valid = titanic.split_frame(ratios=[.8],
...                                    seed=1234)
>>> titanic_xgb = H2OXGBoostEstimator(min_child_weight=16,
...                                   seed=1234)
>>> titanic_xgb.train(x=predictors,
...                   y=response,
...                   training_frame=train,
...                   validation_frame=valid)
>>> print(titanic_xgb.auc(valid=True))

min_data_in_leaf

For tree_method=hist only: the mininum data in a leaf to keep splitting

Type: float (default: 0).

Examples:

>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv")
>>> titanic['survived'] = titanic['survived'].asfactor()
>>> predictors = titanic.columns
>>> del predictors[1:3]
>>> response = 'survived'
>>> train, valid = titanic.split_frame(ratios=[.8],
...                                    seed=1234)
>>> titanic_xgb = H2OXGBoostEstimator(min_data_in_leaf=0.55,
...                                   seed=1234)
>>> titanic_xgb.train(x=predictors,
...                   y=response,
...                   training_frame=train,
...                   validation_frame=valid)
>>> titanic_xgb.auc(valid=True)

min_rows

(same as min_child_weight) Fewest allowed (weighted) observations in a leaf.

Type: float (default: 1).

Examples:

>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv")
>>> titanic['survived'] = titanic['survived'].asfactor()
>>> predictors = titanic.columns
>>> del predictors[1:3]
>>> response = 'survived'
>>> train, valid = titanic.split_frame(ratios=[.8],
...                                    seed=1234)
>>> titanic_xgb = H2OXGBoostEstimator(min_rows=16,
...                                   seed=1234)
>>> titanic_xgb.train(x=predictors,
...                   y=response,
...                   training_frame=train,
...                   validation_frame=valid)
>>> print(titanic_xgb.auc(valid=True))

min_split_improvement

(same as gamma) Minimum relative improvement in squared error reduction for a split to happen

Type: float (default: 0).

Examples:

>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv")
>>> titanic['survived'] = titanic['survived'].asfactor()
>>> predictors = titanic.columns
>>> del predictors[1:3]
>>> response = 'survived'
>>> train, valid = titanic.split_frame(ratios=[.8],
...                                    seed=1234)
>>> titanic_xgb = H2OXGBoostEstimator(min_split_improvement=0.55,
...                                   seed=1234)
>>> titanic_xgb.train(x=predictors,
...                   y=response,
...                   training_frame=train,
...                   validation_frame=valid)
>>> print(titanic_xgb.auc(valid=True))

min_sum_hessian_in_leaf

For tree_method=hist only: the mininum sum of hessian in a leaf to keep splitting

Type: float (default: 100).

Examples:

>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv")
>>> titanic['survived'] = titanic['survived'].asfactor()
>>> predictors = titanic.columns
>>> del predictors[1:3]
>>> response = 'survived'
>>> train, valid = titanic.split_frame(ratios=[.8],
...                                    seed=1234)
>>> titanic_xgb = H2OXGBoostEstimator(min_sum_hessian_in_leaf=90.5,
...                                   seed=1234)
>>> titanic_xgb.train(x=predictors,
...                   y=response,
...                   training_frame=train,
...                   validation_frame=valid)
>>> titanic_xgb.auc(valid=True)

monotone_constraints

A mapping representing monotonic constraints. Use +1 to enforce an increasing constraint and -1 to specify a decreasing constraint.

Type: dict.

Examples:

>>> prostate_hex = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv.zip")
>>> prostate_hex["CAPSULE"] = prostate_hex["CAPSULE"].asfactor()
>>> response = "CAPSULE"
>>> seed=42
>>> monotone_constraints={"AGE":1}
>>> xgb_model = H2OXGBoostEstimator(seed=seed,
...                                 monotone_constraints=monotone_constraints)
>>> xgb_model.train(y=response,
...                 ignored_columns=["ID"],
...                 training_frame=prostate_hex)
>>> xgb_model.scoring_history()

nfolds

Number of folds for K-fold cross-validation (0 to disable or >= 2).

Type: int (default: 0).

Examples:

>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv")
>>> titanic['survived'] = titanic['survived'].asfactor()
>>> predictors = titanic.columns
>>> del predictors[1:3]
>>> response = 'survived'
>>> folds = 5
>>> titanic_xgb = H2OXGBoostEstimator(nfolds=folds,
...                                   seed=1234)
>>> titanic_xgb.train(x=predictors,
...                   y=response,
...                   training_frame=titanic)
>>> titanic_xgb.auc(xval=True)

normalize_type

For booster=dart only: normalize_type

One of: "tree", "forest" (default: "tree").

Examples:

>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv")
>>> titanic['survived'] = titanic['survived'].asfactor()
>>> predictors = titanic.columns
>>> response = 'survived'
>>> train, valid = titanic.split_frame(ratios=[.8],
...                                    seed=1234)
>>> titanic_xgb = H2OXGBoostEstimator(booster='dart',
...                                   normalize_type="tree",
...                                   seed=1234)
>>> titanic_xgb.train(x=predictors,
...                   y=response,
...                   training_frame=train,
...                   validation_frame=valid)
>>> print(titanic_xgb.auc(valid=True))

nthread

Number of parallel threads that can be used to run XGBoost. Cannot exceed H2O cluster limits (-nthreads parameter). Defaults to maximum available

Type: int (default: -1).

Examples:

>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv")
>>> titanic['survived'] = titanic['survived'].asfactor()
>>> predictors = titanic.columns
>>> response = 'survived'
>>> train, valid = titanic.split_frame(ratios=[.8], seed=1234)
>>> thread = 4
>>> titanic_xgb = H2OXGBoostEstimator(nthread=thread,
...                                   seed=1234)
>>> titanic_xgb.train(x=predictors,
...                   y=response,
...                   training_frame=titanic)
>>> print(titanic_xgb.auc(train=True))

ntrees

(same as n_estimators) Number of trees.

Type: int (default: 50).

Examples:

>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv")
>>> titanic['survived'] = titanic['survived'].asfactor()
>>> predictors = titanic.columns
>>> del predictors[1:3]
>>> response = 'survived'
>>> train, valid = titanic.split_frame(ratios=[.8],
...                                    seed=1234)
>>> tree_num = [20, 50, 80, 110, 140, 170, 200]
>>> label = ["20", "50", "80", "110",
...          "140", "170", "200"]
>>> for key, num in enumerate(tree_num):
#              Input integer for 'num' and 'key'
>>> titanic_xgb = H2OXGBoostEstimator(ntrees=num,
...                                   seed=1234)
>>> titanic_xgb.train(x=predictors,
...                   y=response,
...                   training_frame=train,
...                   validation_frame=valid)
>>> print(label[key], 'training score',
...       titanic_xgb.auc(train=True))
>>> print(label[key], 'validation score',
...       titanic_xgb.auc(valid=True))

offset_column

Offset column. This will be added to the combination of columns before applying the link function.

Type: str.

one_drop

For booster=dart only: one_drop

Type: bool (default: False).

Examples:

>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv")
>>> titanic['survived'] = titanic['survived'].asfactor()
>>> predictors = titanic.columns
>>> del predictors[1:3]
>>> response = 'survived'
>>> train, valid = titanic.split_frame(ratios=[.8],
...                                    seed=1234)
>>> titanic_xgb = H2OXGBoostEstimator(booster='dart',
...                                   one_drop=True,
...                                   seed=1234)
>>> titanic_xgb.train(x=predictors,
...                   y=response,
...                   training_frame=train,
...                   validation_frame=valid)
>>> print(titanic_xgb.auc(valid=True))

quiet_mode

Enable quiet mode

Type: bool (default: True).

Examples:

>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv")
>>> titanic['survived'] = titanic['survived'].asfactor()
>>> predictors = titanic.columns
>>> del predictors[1:3]
>>> response = 'survived'
>>> train, valid = titanic.split_frame(ratios=[.8], seed=1234)
>>> titanic_xgb = H2OXGBoostEstimator(seed=1234, quiet_mode=True)
>>> titanic_xgb.train(x=predictors
...                   y=response,
...                   training_frame=train,
...                   validation_frame=valid)
>>> titanic_xgb.mse(valid=True)

rate_drop

For booster=dart only: rate_drop (0..1)

Type: float (default: 0).

Examples:

>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv")
>>> titanic['survived'] = titanic['survived'].asfactor()
>>> predictors = titanic.columns
>>> del predictors[1:3]
>>> response = 'survived'
>>> train, valid = titanic.split_frame(ratios=[.8],
...                                    seed=1234)
>>> titanic_xgb = H2OXGBoostEstimator(rate_drop=0.1, seed=1234)
>>> titanic_xgb.train(x=predictors,
...                   y=response,
...                   training_frame=train,
...                   validation_frame=valid)
>>> print(titanic_xgb.auc(valid=True))

reg_alpha

L1 regularization

Type: float (default: 0).

Examples:

>>> boston = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv")
>>> predictors = boston.columns[:-1]
>>> response = "medv"
>>> boston['chas'] = boston['chas'].asfactor()
>>> train, valid = boston.split_frame(ratios=[.8])
>>> boston_xgb = H2OXGBoostEstimator(reg_alpha=.25)
>>> boston_xgb.train(x=predictors,
...                  y=response,
...                  training_frame=train,
...                  validation_frame=valid)
>>> print(boston_xgb.mse(valid=True))

reg_lambda

L2 regularization

Type: float (default: 1).

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> airlines["Year"] = airlines["Year"].asfactor()
>>> airlines["Month"] = airlines["Month"].asfactor()
>>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
>>> airlines["Cancelled"] = airlines["Cancelled"].asfactor()
>>> airlines['FlightNum'] = airlines['FlightNum'].asfactor()
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> response = "IsDepDelayed"
>>> train, valid= airlines.split_frame(ratios=[.8])
>>> airlines_xgb = H2OXGBoostEstimator(reg_lambda=.0001,
...                                    seed=1234)
>>> airlines_xgb.train(x=predictors,
...                    y=response,
...                    training_frame=train,
...                    validation_frame=valid)
>>> print(airlines_xgb.auc(valid=True))

response_column

Response variable column.

Type: str.

sample_rate

(same as subsample) Row sample rate per tree (from 0.0 to 1.0)

Type: float (default: 1).

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> airlines["Year"] = airlines["Year"].asfactor()
>>> airlines["Month"] = airlines["Month"].asfactor()
>>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
>>> airlines["Cancelled"] = airlines["Cancelled"].asfactor()
>>> airlines['FlightNum'] = airlines['FlightNum'].asfactor()
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> response = "IsDepDelayed"
>>> train, valid= airlines.split_frame(ratios=[.8],
...                                    seed=1234)
>>> airlines_xgb = H2OXGBoostEstimator(sample_rate=.7,
...                                    seed=1234)
>>> airlines_xgb.train(x=predictors,
...                    y=response,
...                    training_frame=train,
...                    validation_frame=valid)
>>> print(airlines_xgb.auc(valid=True))

sample_type

For booster=dart only: sample_type

One of: "uniform", "weighted" (default: "uniform").

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> airlines["Year"]= airlines["Year"].asfactor()
>>> airlines["Month"]= airlines["Month"].asfactor()
>>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
>>> airlines["Cancelled"] = airlines["Cancelled"].asfactor()
>>> airlines['FlightNum'] = airlines['FlightNum'].asfactor()
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> response = "IsDepDelayed"
>>> train, valid= airlines.split_frame(ratios=[.8],
...                                    seed=1234)
>>> airlines_xgb = H2OXGBoostEstimator(sample_type="weighted",
...                                    seed=1234)
>>> airlines_xgb.train(x=predictors,
...                    y=response,
...                    training_frame=train,
...                    validation_frame=valid)
>>> print(airlines_xgb.auc(valid=True))

save_matrix_directory

Directory where to save matrices passed to XGBoost library. Useful for debugging.

Type: str.

score_each_iteration

Whether to score during each iteration of model training.

Type: bool (default: False).

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> airlines["Year"] = airlines["Year"].asfactor()
>>> airlines["Month"] = airlines["Month"].asfactor()
>>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
>>> airlines["Cancelled"] = airlines["Cancelled"].asfactor()
>>> airlines['FlightNum'] = airlines['FlightNum'].asfactor()
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> response = "IsDepDelayed"
>>> train, valid= airlines.split_frame(ratios=[.8],
...                                    seed=1234)
>>> airlines_xgb = H2OXGBoostEstimator(score_each_iteration=True,
...                                    ntrees=55,
...                                    seed=1234)
>>> airlines_xgb.train(x=predictors,
...                    y=response,
...                    training_frame=train,
...                    validation_frame=valid)
>>> airlines_xgb.scoring_history()

score_tree_interval

Score the model after every so many trees. Disabled if set to 0.

Type: int (default: 0).

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> airlines["Year"] = airlines["Year"].asfactor()
>>> airlines["Month"] = airlines["Month"].asfactor()
>>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
>>> airlines["Cancelled"] = airlines["Cancelled"].asfactor()
>>> airlines['FlightNum'] = airlines['FlightNum'].asfactor()
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> response = "IsDepDelayed"
>>> train, valid= airlines.split_frame(ratios=[.8],
...                                    seed=1234)
>>> airlines_xgb = H2OXGBoostEstimator(score_tree_interval=5,
...                                    seed=1234)
>>> airlines_xgb.train(x=predictors,
...                    y=response,
...                    training_frame=train,
...                    validation_frame=valid)
>>> airlines_xgb.scoring_history()

seed

Seed for pseudo random number generator (if applicable)

Type: int (default: -1).

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> airlines["Year"] = airlines["Year"].asfactor()
>>> airlines["Month"] = airlines["Month"].asfactor()
>>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
>>> airlines["Cancelled"] = airlines["Cancelled"].asfactor()
>>> airlines['FlightNum'] = airlines['FlightNum'].asfactor()
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> response = "IsDepDelayed"
>>> train, valid= airlines.split_frame(ratios=[.8], seed=1234)
>>> xgb_w_seed_1 = H2OXGBoostEstimator(col_sample_rate=.7,
...                                    seed=1234)
>>> xgb_w_seed_1.train(x=predictors,
...                    y=response,
...                    training_frame=train,
...                    validation_frame=valid)
>>> xgb_w_seed_2 = H2OXGBoostEstimator(col_sample_rate = .7,
...                                    seed = 1234)
>>> xgb_w_seed_2.train(x=predictors,
...                    y=response,
...                    training_frame=train,
...                    validation_frame=valid)
>>> print('auc for the 1st model built with a seed:',
...        xgb_w_seed_1.auc(valid=True))
>>> print('auc for the 2nd model built with a seed:',
...        xgb_w_seed_2.auc(valid=True))

skip_drop

For booster=dart only: skip_drop (0..1)

Type: float (default: 0).

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> airlines["Year"] = airlines["Year"].asfactor()
>>> airlines["Month"] = airlines["Month"].asfactor()
>>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
>>> airlines["Cancelled"] = airlines["Cancelled"].asfactor()
>>> airlines['FlightNum'] = airlines['FlightNum'].asfactor()
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> response = "IsDepDelayed"
>>> train, valid= airlines.split_frame(ratios=[.8],
...                                    seed=1234)
>>> airlines_xgb = H2OXGBoostEstimator(skip_drop=0.5,
...                                    seed=1234)
>>> airlines_xgb.train(x=predictors,
...                    y=response,
...                    training_frame=train)
>>> airlines_xgb.auc(train=True)

stopping_metric

Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anonomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client.

One of: "auto", "deviance", "logloss", "mse", "rmse", "mae", "rmsle", "auc", "aucpr", "lift_top_group", "misclassification", "mean_per_class_error", "custom", "custom_increasing" (default: "auto").

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> airlines["Year"] = airlines["Year"].asfactor()
>>> airlines["Month"] = airlines["Month"].asfactor()
>>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
>>> airlines["Cancelled"] = airlines["Cancelled"].asfactor()
>>> airlines['FlightNum'] = airlines['FlightNum'].asfactor()
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> response = "IsDepDelayed"
>>> train, valid= airlines.split_frame(ratios=[.8], seed=1234)
>>> airlines_xgb = H2OXGBoostEstimator(stopping_metric="auc",
...                                    stopping_rounds=3,
...                                    stopping_tolerance=1e-2,
...                                    seed=1234)
>>> airlines_xgb.train(x=predictors,
...                    y=response,
...                    training_frame=train,
...                    validation_frame=valid)
>>> airlines_xgb.auc(valid=True)

stopping_rounds

Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable)

Type: int (default: 0).

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> airlines["Year"] = airlines["Year"].asfactor()
>>> airlines["Month"] = airlines["Month"].asfactor()
>>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
>>> airlines["Cancelled"] = airlines["Cancelled"].asfactor()
>>> airlines['FlightNum'] = airlines['FlightNum'].asfactor()
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> response = "IsDepDelayed"
>>> train, valid= airlines.split_frame(ratios=[.8],
...                                    seed=1234)
>>> airlines_xgb = H2OXGBoostEstimator(stopping_metric="auc",
...                                    stopping_rounds=3,
...                                    stopping_tolerance=1e-2,
...                                    seed=1234)
>>> airlines_xgb.train(x=predictors,
...                    y=response,
...                    training_frame=train,
...                    validation_frame=valid)
>>> airlines_xgb.auc(valid=True)

stopping_tolerance

Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much)

Type: float (default: 0.001).

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> airlines["Year"] = airlines["Year"].asfactor()
>>> airlines["Month"] = airlines["Month"].asfactor()
>>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
>>> airlines["Cancelled"] = airlines["Cancelled"].asfactor()
>>> airlines['FlightNum'] = airlines['FlightNum'].asfactor()
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> response = "IsDepDelayed"
>>> train, valid= airlines.split_frame(ratios=[.8],
...                                    seed=1234)
>>> airlines_xgb = H2OXGBoostEstimator(stopping_metric="auc",
...                                    stopping_rounds=3,
...                                    stopping_tolerance=1e-2,
...                                    seed=1234)
>>> airlines_xgb.train(x=predictors,
...                    y=response,
...                    training_frame=train,
...                    validation_frame=valid)
>>> airlines_xgb.auc(valid=True)

subsample

(same as sample_rate) Row sample rate per tree (from 0.0 to 1.0)

Type: float (default: 1).

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> airlines["Year"] = airlines["Year"].asfactor()
>>> airlines["Month"] = airlines["Month"].asfactor()
>>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
>>> airlines["Cancelled"] = airlines["Cancelled"].asfactor()
>>> airlines['FlightNum'] = airlines['FlightNum'].asfactor()
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> response = "IsDepDelayed"
>>> train, valid= airlines.split_frame(ratios=[.8],
...                                    seed=1234)
>>> airlines_xgb = H2OXGBoostEstimator(sample_rate=.7,
...                                    seed=1234)
>>> airlines_xgb.train(x=predictors,
...                    y=response,
...                    training_frame=train,
...                    validation_frame=valid)
>>> print(airlines_xgb.auc(valid=True))

training_frame

Id of the training data frame.

Type: H2OFrame.

Examples:

>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv")
>>> titanic['survived'] = titanic['survived'].asfactor()
>>> predictors = titanic.columns
>>> del predictors[1:3]
>>> response = 'survived'
>>> train, valid = titanic.split_frame(ratios=[.8],
...                                    seed=1234)
>>> titanic_xgb = H2OXGBoostEstimator(seed=1234)
>>> titanic_xgb.train(x=predictors,
...                   y=response,
...                   training_frame=train,
...                   validation_frame=valid)
>>> titanic_xgb.auc(valid=True)

tree_method

Tree method

One of: "auto", "exact", "approx", "hist" (default: "auto").

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> airlines["Year"] = airlines["Year"].asfactor()
>>> airlines["Month"] = airlines["Month"].asfactor()
>>> airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
>>> airlines["Cancelled"] = airlines["Cancelled"].asfactor()
>>> airlines['FlightNum'] = airlines['FlightNum'].asfactor()
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> response = "IsDepDelayed"
>>> train, valid= airlines.split_frame(ratios=[.8],
...                                    seed=1234)
>>> >>> airlines_xgb = H2OXGBoostEstimator(seed=1234,
...                                        tree_method="approx")
>>> airlines_xgb.train(x=predictors,
...                    y=response,
...                    training_frame=train,
...                    validation_frame=valid)
>>> print(airlines_xgb.auc(valid=True))

tweedie_power

Tweedie power for Tweedie regression, must be between 1 and 2.

Type: float (default: 1.5).

Examples:

>>> insurance = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/insurance.csv")
>>> predictors = insurance.columns[0:4]
>>> response = 'Claims'
>>> insurance['Group'] = insurance['Group'].asfactor()
>>> insurance['Age'] = insurance['Age'].asfactor()
>>> train, valid = insurance.split_frame(ratios=[.8],
...                                      seed=1234)
>>> insurance_xgb = H2OXGBoostEstimator(distribution="tweedie",
...                                     tweedie_power=1.2,
...                                     seed=1234)
>>> insurance_xgb.train(x=predictors,
...                     y=response,
...                     training_frame=train,
...                     validation_frame=valid)
>>> print(insurance_xgb.mse(valid=True))

validation_frame

Id of the validation data frame.

Type: H2OFrame.

Examples:

>>> insurance = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/insurance.csv")
>>> insurance['Group'] = insurance['Group'].asfactor()
>>> insurance['Age'] = insurance['Age'].asfactor()
>>> predictors = insurance.columns[0:4]
>>> response = 'Claims'
>>> train, valid = insurance.split_frame(ratios=[.8],
...                                      seed=1234)
>>> insurance_xgb = H2OXGBoostEstimator(seed=1234)
>>> insurance_xgb.train(x=predictors,
...                     y=response,
...                     training_frame=train,
...                     validation_frame=valid)
>>> print(insurance_xgb.mse(valid=True))

weights_column

Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor.

Type: str.

Examples:

>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv")
>>> titanic['survived'] = titanic['survived'].asfactor()
>>> predictors = titanic.columns
>>> del predictors[1:3]
>>> response = 'survived'
>>> train, valid = titanic.split_frame(ratios=[.8],
...                                    seed=1234)
>>> titanic_xgb = H2OXGBoostEstimator(seed=1234)
>>> titanic_xgb.train(x=predictors,
...                   y=response,
...                   training_frame=train,
...                   validation_frame=valid)
>>> titanic_xgb.auc(valid=True)

Unsupervised

H2OAggregatorEstimator

class h2o.estimators.aggregator.H2OAggregatorEstimator(**kwargs)

Bases: h2o.estimators.estimator_base.H2OEstimator

Aggregator

categorical_encoding

Encoding scheme for categorical features

One of: "auto", "enum", "one_hot_internal", "one_hot_explicit", "binary", "eigen", "label_encoder", "sort_by_response", "enum_limited" (default: "auto").

Examples:

>>> df = h2o.create_frame(rows=10000,
...                       cols=10,
...                       categorical_fraction=0.6,
...                       integer_fraction=0,
...                       binary_fraction=0,
...                       real_range=100,
...                       integer_range=100,
...                       missing_fraction=0,
...                       factors=100,
...                       seed=1234)
>>> params = {"target_num_exemplars": 1000,
...           "rel_tol_num_exemplars": 0.5,
...           "categorical_encoding": "eigen"}
>>> agg = H2OAggregatorEstimator(**params)
>>> agg.train(training_frame=df)
>>> new_df = agg.aggregated_frame
>>> new_df

export_checkpoints_dir

Automatically export generated models to this directory.

Type: str.

Examples:

>>> import tempfile
>>> from os import listdir
>>> df = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> checkpoints_dir = tempfile.mkdtemp()
>>> model = H2OAggregatorEstimator(target_num_exemplars=500, 
...                                rel_tol_num_exemplars=0.3,
...                                export_checkpoints_dir=checkpoints_dir)
>>> model.train(training_frame=df)
>>> new_df = model.aggregated_frame
>>> new_df
>>> len(listdir(checkpoints_dir))

ignore_const_cols

Ignore constant columns.

Type: bool (default: True).

Examples:

>>> df = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> params = {"ignore_const_cols": False,
...           "target_num_exemplars": 500,
...           "rel_tol_num_exemplars": 0.3,
...           "transform": "standardize",
...           "categorical_encoding": "eigen"}
>>> model = H2OAggregatorEstimator(**params)
>>> model.train(training_frame=df)
>>> new_df = model.aggregated_frame
>>> new_df

ignored_columns

Names of columns to ignore for training.

Type: List[str].

num_iteration_without_new_exemplar

The number of iterations to run before aggregator exits if the number of exemplars collected didn’t change

Type: int (default: 500).

Examples:

>>> df = h2o.create_frame(rows=10000,
...                       cols=10,
...                       categorical_fraction=0.6,
...                       integer_fraction=0,
...                       binary_fraction=0,
...                       real_range=100,
...                       integer_range=100,
...                       missing_fraction=0,
...                       factors=100,
...                       seed=1234)
>>> params = {"target_num_exemplars": 1000,
...           "rel_tol_num_exemplars": 0.5,
...           "categorical_encoding": "eigen",
...           "num_iteration_without_new_exemplar": 400}
>>> agg = H2OAggregatorEstimator(**params)
>>> agg.train(training_frame=df)
>>> new_df = agg.aggregated_frame
>>> new_df

rel_tol_num_exemplars

Relative tolerance for number of exemplars (e.g, 0.5 is +/- 50 percents)

Type: float (default: 0.5).

Examples:

>>> df = h2o.create_frame(rows=10000,
...                       cols=10,
...                       categorical_fraction=0.6,
...                       integer_fraction=0,
...                       binary_fraction=0,
...                       real_range=100,
...                       integer_range=100,
...                       missing_fraction=0,
...                       factors=100,
...                       seed=1234)
>>> params = {"target_num_exemplars": 1000,
...           "rel_tol_num_exemplars": 0.5,
...           "categorical_encoding": "eigen",
...           "num_iteration_without_new_exemplar": 400}
>>> agg = H2OAggregatorEstimator(**params)
>>> agg.train(training_frame=df)
>>> new_df = agg.aggregated_frame
>>> new_df

response_column

Response variable column.

Type: str.

save_mapping_frame

Whether to export the mapping of the aggregated frame

Type: bool (default: False).

Examples:

>>> df = h2o.create_frame(rows=10000,
...                       cols=10,
...                       categorical_fraction=0.6,
...                       integer_fraction=0,
...                       binary_fraction=0,
...                       real_range=100,
...                       integer_range=100,
...                       missing_fraction=0,
...                       factors=100,
...                       seed=1234)
>>> params = {"target_num_exemplars": 1000,
...           "rel_tol_num_exemplars": 0.5,
...           "categorical_encoding": "eigen",
...           "save_mapping_frame": True}
>>> agg = H2OAggregatorEstimator(**params)
>>> agg.train(training_frame=df)
>>> new_df = agg.aggregated_frame
>>> new_df

target_num_exemplars

Targeted number of exemplars

Type: int (default: 5000).

Examples:

>>> df = h2o.create_frame(rows=10000,
...                       cols=10,
...                       categorical_fraction=0.6,
...                       integer_fraction=0,
...                       binary_fraction=0,
...                       real_range=100,
...                       integer_range=100,
...                       missing_fraction=0,
...                       factors=100,
...                       seed=1234)
>>> params = {"target_num_exemplars": 1000,
...           "rel_tol_num_exemplars": 0.5,
...           "categorical_encoding": "eigen",
...           "num_iteration_without_new_exemplar": 400}
>>> agg = H2OAggregatorEstimator(**params)
>>> agg.train(training_frame=df)
>>> new_df = agg.aggregated_frame
>>> new_df

training_frame

Id of the training data frame.

Type: H2OFrame.

Examples:

>>> df = h2o.create_frame(rows=10000,
...                       cols=10,
...                       categorical_fraction=0.6,
...                       integer_fraction=0,
...                       binary_fraction=0,
...                       real_range=100,
...                       integer_range=100,
...                       missing_fraction=0,
...                       factors=100,
...                       seed=1234)
>>> params = {"target_num_exemplars": 1000,
...           "rel_tol_num_exemplars": 0.5,
...           "categorical_encoding": "eigen",
...           "num_iteration_without_new_exemplar": 400}
>>> agg = H2OAggregatorEstimator(**params)
>>> agg.train(training_frame=df)
>>> new_df = agg.aggregated_frame
>>> new_df

transform

Transformation of training data

One of: "none", "standardize", "normalize", "demean", "descale" (default: "normalize").

Examples:

>>> df = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> params = {"ignore_const_cols": False,
...           "target_num_exemplars": 500,
...           "rel_tol_num_exemplars": 0.3,
...           "transform": "standardize",
...           "categorical_encoding": "eigen"}
>>> model = H2OAggregatorEstimator(**params)
>>> model.train(training_frame=df)
>>> new_df = model.aggregated_frame

H2OAutoEncoderEstimator

class h2o.estimators.deeplearning.H2OAutoEncoderEstimator(**kwargs)

Bases: h2o.estimators.deeplearning.H2ODeepLearningEstimator

Examples:

>>> import h2o as ml
>>> from h2o.estimators.deeplearning import H2OAutoEncoderEstimator
>>> ml.init()
>>> rows = [[1,2,3,4,0]*50, [2,1,2,4,1]*50, [2,1,4,2,1]*50, [0,1,2,34,1]*50, [2,3,4,1,0]*50]
>>> fr = ml.H2OFrame(rows)
>>> fr[4] = fr[4].asfactor()
>>> model = H2OAutoEncoderEstimator()
>>> model.train(x=range(4), training_frame=fr)

H2OGenericEstimator

class h2o.estimators.generic.H2OGenericEstimator(**kwargs)

Bases: h2o.estimators.estimator_base.H2OEstimator

Import MOJO Model

static from_file(file=<class 'str'>)

Creates new Generic model by loading existing embedded model into library, e.g. from H2O MOJO. The imported model must be supported by H2O.

Parameters:file – A string containing path to the file to create the model from Returns:H2OGenericEstimator instance representing the generic model Examples:

>>> from h2o.estimators import H2OIsolationForestEstimator, H2OGenericEstimator
>>> import tempfile
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/airlines_train.csv")
>>> ifr = H2OIsolationForestEstimator(ntrees=1)
>>> ifr.train(x=["Origin","Dest"], y="Distance", training_frame=airlines)
>>> original_model_filename = tempfile.mkdtemp()
>>> original_model_filename = ifr.download_mojo(original_model_filename)
>>> model = H2OGenericEstimator.from_file(original_model_filename)
>>> model.model_performance()

model_key

Key to the self-contained model archive already uploaded to H2O.

Type: H2OFrame.

Examples:

>>> from h2o.estimators import H2OGenericEstimator, H2OXGBoostEstimator
>>> import tempfile
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/airlines_train.csv")
>>> y = "IsDepDelayed"
>>> x = ["fYear","fMonth","Origin","Dest","Distance"]
>>> xgb = H2OXGBoostEstimator(ntrees=1, nfolds=3)
>>> xgb.train(x=x, y=y, training_frame=airlines)
>>> original_model_filename = tempfile.mkdtemp()
>>> original_model_filename = xgb.download_mojo(original_model_filename)
>>> key = h2o.lazy_import(original_model_filename)
>>> fr = h2o.get_frame(key[0])
>>> model = H2OGenericEstimator(model_key=fr)
>>> model.train()
>>> model.auc()

path

Path to file with self-contained model archive.

Type: str.

Examples:

>>> from h2o.estimators import H2OIsolationForestEstimator, H2OGenericEstimator
>>> import tempfile
>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/airlines_train.csv")
>>> ifr = H2OIsolationForestEstimator(ntrees=1)
>>> ifr.train(x=["Origin","Dest"], y="Distance", training_frame=airlines)
>>> generic_mojo_filename = tempfile.mkdtemp("zip","genericMojo")
>>> generic_mojo_filename = model.download_mojo(path=generic_mojo_filename)
>>> model = H2OGenericEstimator.from_file(generic_mojo_filename)
>>> model.model_performance()

H2OGeneralizedLowRankEstimator

class h2o.estimators.glrm.H2OGeneralizedLowRankEstimator(**kwargs)

Bases: h2o.estimators.estimator_base.H2OEstimator

Generalized Low Rank Modeling

Builds a generalized low rank model of a H2O dataset.

expand_user_y

Expand categorical columns in user-specified initial Y

Type: bool (default: True).

Examples:

>>> iris = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_wheader.csv")
>>> rank = 3
>>> gx = 0.5
>>> gy = 0.5
>>> trans = "standardize"
>>> iris_glrm = H2OGeneralizedLowRankEstimator(k=rank,
...                                            loss="Quadratic",
...                                            gamma_x=gx,
...                                            gamma_y=gy,
...                                            transform=trans,
...                                            expand_user_y=False)
>>> iris_glrm.train(x=iris.names, training_frame=iris)
>>> iris_glrm.show()

export_checkpoints_dir

Automatically export generated models to this directory.

Type: str.

Examples:

>>> import tempfile
>>> from os import listdir
>>> iris = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_wheader.csv")
>>> checkpoints_dir = tempfile.mkdtemp()
>>> iris_glrm = H2OGeneralizedLowRankEstimator(k=3,
...                                            export_checkpoints_dir=checkpoints_dir,
...                                            seed=1234)
>>> iris_glrm.train(x=iris.names, training_frame=iris)
>>> len(listdir(checkpoints_dir))

gamma_x

Regularization weight on X matrix

Type: float (default: 0).

Examples:

>>> iris = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_wheader.csv")
>>> rank = 3
>>> gx = 0.5
>>> gy = 0.5
>>> trans = "standardize"
>>> iris_glrm = H2OGeneralizedLowRankEstimator(k=rank,
...                                            loss="Quadratic",
...                                            gamma_x=gx,
...                                            gamma_y=gy,
...                                            transform=trans)
>>> iris_glrm.train(x=iris.names, training_frame=iris)
>>> iris_glrm.show()

gamma_y

Regularization weight on Y matrix

Type: float (default: 0).

Examples:

>>> iris = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_wheader.csv")
>>> rank = 3
>>> gx = 0.5
>>> gy = 0.5
>>> trans = "standardize"
>>> iris_glrm = H2OGeneralizedLowRankEstimator(k=rank,
...                                            loss="Quadratic",
...                                            gamma_x=gx,
...                                            gamma_y=gy,
...                                            transform=trans)
>>> iris_glrm.train(x=iris.names, training_frame=iris)
>>> iris_glrm.show()

ignore_const_cols

Ignore constant columns.

Type: bool (default: True).

Examples:

>>> iris = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_wheader.csv")
>>> iris_glrm = H2OGeneralizedLowRankEstimator(k=3,
...                                            ignore_const_cols=False,
...                                            seed=1234)
>>> iris_glrm.train(x=iris.names, training_frame=iris)
>>> iris_glrm.show()

ignored_columns

Names of columns to ignore for training.

Type: List[str].

impute_original

Reconstruct original training data by reversing transform

Type: bool (default: False).

Examples:

>>> iris = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_wheader.csv")
>>> rank = 3
>>> gx = 0.5
>>> gy = 0.5
>>> trans = "standardize"
>>> iris_glrm = H2OGeneralizedLowRankEstimator(k=rank,
...                                            loss="Quadratic",
...                                            gamma_x=gx,
...                                            gamma_y=gy,
...                                            transform=trans
...                                            impute_original=True)
>>> iris_glrm.train(x=iris.names, training_frame=iris)
>>> iris_glrm.show()

init

Initialization mode

One of: "random", "svd", "plus_plus", "user" (default: "plus_plus").

Examples:

>>> iris = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_wheader.csv")
>>> iris_glrm = H2OGeneralizedLowRankEstimator(k=3,
...                                            init="svd",
...                                            seed=1234) 
>>> iris_glrm.train(x=iris.names, training_frame=iris)
>>> iris_glrm.show()

init_step_size

Initial step size

Type: float (default: 1).

Examples:

>>> iris = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_wheader.csv")
>>> iris_glrm = H2OGeneralizedLowRankEstimator(k=3,
...                                            init_step_size=2.5,
...                                            seed=1234) 
>>> iris_glrm.train(x=iris.names, training_frame=iris)
>>> iris_glrm.show()

k

Rank of matrix approximation

Type: int (default: 1).

Examples:

>>> iris = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_wheader.csv")
>>> iris_glrm = H2OGeneralizedLowRankEstimator(k=3)
>>> iris_glrm.train(x=iris.names, training_frame=iris)
>>> iris_glrm.show()

loading_name

Frame key to save resulting X

Type: str.

Examples:

>>> acs = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/bigdata/laptop/census/ACS_13_5YR_DP02_cleaned.zip")
>>> acs_fill = acs.drop("ZCTA5")
>>> acs_glrm = H2OGeneralizedLowRankEstimator(k=10,
...                                           transform="standardize",
...                                           loss="quadratic",
...                                           regularization_x="quadratic",
...                                           regularization_y="L1",
...                                           gamma_x=0.25,
...                                           gamma_y=0.5,
...                                           max_iterations=1,
...                                           loading_name="acs_full")
>>> acs_glrm.train(x=acs_fill.names, training_frame=acs)
>>> acs_glrm.loading_name
>>> acs_glrm.show()

loss

Numeric loss function

One of: "quadratic", "absolute", "huber", "poisson", "hinge", "logistic", "periodic" (default: "quadratic").

Examples:

>>> acs = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/bigdata/laptop/census/ACS_13_5YR_DP02_cleaned.zip")
>>> acs_fill = acs.drop("ZCTA5")
>>> acs_glrm = H2OGeneralizedLowRankEstimator(k=10,
...                                           transform="standardize",
...                                           loss="absolute",
...                                           regularization_x="quadratic",
...                                           regularization_y="L1",
...                                           gamma_x=0.25,
...                                           gamma_y=0.5,
...                                           max_iterations=700)
>>> acs_glrm.train(x=acs_fill.names, training_frame=acs)
>>> acs_glrm.show()

loss_by_col

Loss function by column (override)

Type: List[Enum["quadratic", "absolute", "huber", "poisson", "hinge", "logistic", "periodic", "categorical", "ordinal"]].

Examples:

>>> arrestsH2O = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/pca_test/USArrests.csv")
>>> arrests_glrm = H2OGeneralizedLowRankEstimator(k=3,
...                                               loss="quadratic",
...                                               loss_by_col=["absolute","huber"],
...                                               loss_by_col_idx=[0,3],
...                                               regularization_x="quadratic",
...                                               regularization_y="l1")
>>> arrests_glrm.train(x=arrestsH2O.names, training_frame=arrestsH2O)
>>> arrests_glrm.show()

loss_by_col_idx

Loss function by column index (override)

Type: List[int].

Examples:

>>> arrestsH2O = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/pca_test/USArrests.csv")
>>> arrests_glrm = H2OGeneralizedLowRankEstimator(k=3,
...                                               loss="quadratic",
...                                               loss_by_col=["absolute","huber"],
...                                               loss_by_col_idx=[0,3],
...                                               regularization_x="quadratic",
...                                               regularization_y="l1")
>>> arrests_glrm.train(x=arrestsH2O.names, training_frame=arrestsH2O)
>>> arrests_glrm.show()

max_iterations

Maximum number of iterations

Type: int (default: 1000).

Examples:

>>> acs = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/bigdata/laptop/census/ACS_13_5YR_DP02_cleaned.zip")
>>> acs_fill = acs.drop("ZCTA5")
>>> acs_glrm = H2OGeneralizedLowRankEstimator(k=10,
...                                           transform="standardize",
...                                           loss="quadratic",
...                                           regularization_x="quadratic",
...                                           regularization_y="L1",
...                                           gamma_x=0.25,
...                                           gamma_y=0.5,
...                                           max_iterations=700)
>>> acs_glrm.train(x=acs_fill.names, training_frame=acs)
>>> acs_glrm.show()

max_runtime_secs

Maximum allowed runtime in seconds for model training. Use 0 to disable.

Type: float (default: 0).

Examples:

>>> arrestsH2O = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/pca_test/USArrests.csv")
>>> arrests_glrm = H2OGeneralizedLowRankEstimator(k=3,
...                                               max_runtime_secs=15,
...                                               max_iterations=500,
...                                               max_updates=900,
...                                               min_step_size=0.005)
>>> arrests_glrm.train(x=arrestsH2O.names, training_frame=arrestsH2O)
>>> arrests_glrm.show()

max_updates

Maximum number of updates, defaults to 2*max_iterations

Type: int (default: 2000).

Examples:

>>> arrestsH2O = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/pca_test/USArrests.csv")
>>> arrests_glrm = H2OGeneralizedLowRankEstimator(k=3,
...                                               max_runtime_secs=15,
...                                               max_iterations=500,
...                                               max_updates=900,
...                                               min_step_size=0.005)
>>> arrests_glrm.train(x=arrestsH2O.names, training_frame=arrestsH2O)
>>> arrests_glrm.show()

min_step_size

Minimum step size

Type: float (default: 0.0001).

Examples:

>>> arrestsH2O = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/pca_test/USArrests.csv")
>>> arrests_glrm = H2OGeneralizedLowRankEstimator(k=3,
...                                               max_runtime_secs=15,
...                                               max_iterations=500,
...                                               max_updates=900,
...                                               min_step_size=0.005)
>>> arrests_glrm.train(x=arrestsH2O.names, training_frame=arrestsH2O)
>>> arrests_glrm.show()

multi_loss

Categorical loss function

One of: "categorical", "ordinal" (default: "categorical").

Examples:

>>> arrestsH2O = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/pca_test/USArrests.csv")
>>> arrests_glrm = H2OGeneralizedLowRankEstimator(k=3,
...                                               loss="quadratic",
...                                               loss_by_col=["absolute","huber"],
...                                               loss_by_col_idx=[0,3],
...                                               regularization_x="quadratic",
...                                               regularization_y="l1"
...                                               multi_loss="ordinal")
>>> arrests_glrm.train(x=arrestsH2O.names, training_frame=arrestsH2O)
>>> arrests_glrm.show()

period

Length of period (only used with periodic loss function)

Type: int (default: 1).

Examples:

>>> arrestsH2O = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/pca_test/USArrests.csv")
>>> arrests_glrm = H2OGeneralizedLowRankEstimator(k=3,
...                                               max_runtime_secs=15,
...                                               max_iterations=500,
...                                               max_updates=900,
...                                               min_step_size=0.005,
...                                               period=5)
>>> arrests_glrm.train(x=arrestsH2O.names, training_frame=arrestsH2O)
>>> arrests_glrm.show()

recover_svd

Recover singular values and eigenvectors of XY

Type: bool (default: False).

Examples:

>>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate_cat.csv")
>>> prostate[0] = prostate[0].asnumeric()
>>> prostate[4] = prostate[4].asnumeric()
>>> loss_all = ["Hinge", "Quadratic", "Categorical", "Categorical",
...             "Hinge", "Quadratic", "Quadratic", "Quadratic"]
>>> pros_glrm = H2OGeneralizedLowRankEstimator(k=5,
...                                            loss_by_col=loss_all,
...                                            recover_svd=True,
...                                            transform="standardize",
...                                            seed=12345)
>>> pros_glrm.train(x=prostate.names, training_frame=prostate)
>>> pros_glrm.show()

regularization_x

Regularization function for X matrix

One of: "none", "quadratic", "l2", "l1", "non_negative", "one_sparse", "unit_one_sparse", "simplex" (default: "none").

Examples:

>>> arrestsH2O = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/pca_test/USArrests.csv")
>>> arrests_glrm = H2OGeneralizedLowRankEstimator(k=3,
...                                               loss="quadratic",
...                                               loss_by_col=["absolute","huber"],
...                                               loss_by_col_idx=[0,3],
...                                               regularization_x="quadratic",
...                                               regularization_y="l1")
>>> arrests_glrm.train(x=arrestsH2O.names, training_frame=arrestsH2O)
>>> arrests_glrm.show()

regularization_y

Regularization function for Y matrix

One of: "none", "quadratic", "l2", "l1", "non_negative", "one_sparse", "unit_one_sparse", "simplex" (default: "none").

Examples:

>>> arrestsH2O = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/pca_test/USArrests.csv")
>>> arrests_glrm = H2OGeneralizedLowRankEstimator(k=3,
...                                               loss="quadratic",
...                                               loss_by_col=["absolute","huber"],
...                                               loss_by_col_idx=[0,3],
...                                               regularization_x="quadratic",
...                                               regularization_y="l1")
>>> arrests_glrm.train(x=arrestsH2O.names, training_frame=arrestsH2O)
>>> arrests_glrm.show()

score_each_iteration

Whether to score during each iteration of model training.

Type: bool (default: False).

Examples:

>>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate_cat.csv")
>>> prostate[0] = prostate[0].asnumeric()
>>> prostate[4] = prostate[4].asnumeric()
>>> loss_all = ["Hinge", "Quadratic", "Categorical", "Categorical",
...             "Hinge", "Quadratic", "Quadratic", "Quadratic"]
>>> pros_glrm = H2OGeneralizedLowRankEstimator(k=5,
...                                            loss_by_col=loss_all,
...                                            score_each_iteration=True,
...                                            transform="standardize",
...                                            seed=12345)
>>> pros_glrm.train(x=prostate.names, training_frame=prostate)
>>> pros_glrm.show()

seed

RNG seed for initialization

Type: int (default: -1).

Examples:

>>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate_cat.csv")
>>> prostate[0] = prostate[0].asnumeric()
>>> prostate[4] = prostate[4].asnumeric()
>>> glrm_w_seed = H2OGeneralizedLowRankEstimator(k=5, seed=12345) 
>>> glrm_w_seed.train(x=prostate.names, training_frame=prostate)
>>> glrm_wo_seed = H2OGeneralizedLowRankEstimator(k=5, 
>>> glrm_wo_seed.train(x=prostate.names, training_frame=prostate)
>>> glrm_w_seed.show()
>>> glrm_wo_seed.show()

svd_method

Method for computing SVD during initialization (Caution: Randomized is currently experimental and unstable)

One of: "gram_s_v_d", "power", "randomized" (default: "randomized").

Examples:

>>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate_cat.csv")
>>> prostate[0] = prostate[0].asnumeric()
>>> prostate[4] = prostate[4].asnumeric()
>>> pros_glrm = H2OGeneralizedLowRankEstimator(k=5,
...                                            svd_method="power",
...                                            seed=1234)
>>> pros_glrm.train(x=prostate.names, training_frame=prostate)
>>> pros_glrm.show()

training_frame

Id of the training data frame.

Type: H2OFrame.

Examples:

>>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate_cat.csv")
>>> prostate[0] = prostate[0].asnumeric()
>>> prostate[4] = prostate[4].asnumeric()
>>> pros_glrm = H2OGeneralizedLowRankEstimator(k=5,
...                                            seed=1234)
>>> pros_glrm.train(x=prostate.names, training_frame=prostate)
>>> pros_glrm.show()

transform

Transformation of training data

One of: "none", "standardize", "normalize", "demean", "descale" (default: "none").

Examples:

>>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate_cat.csv")
>>> prostate[0] = prostate[0].asnumeric()
>>> prostate[4] = prostate[4].asnumeric()
>>> pros_glrm = H2OGeneralizedLowRankEstimator(k=5,
...                                            score_each_iteration=True,
...                                            transform="standardize",
...                                            seed=12345)
>>> pros_glrm.train(x=prostate.names, training_frame=prostate)
>>> pros_glrm.show()

user_x

User-specified initial X

Type: H2OFrame.

Examples:

>>> arrestsH2O = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/USArrests.csv")
>>> initial_x = ([[5.412, 65.24, -7.54, -0.032, 2.212, 92.24, -17.54, 23.268, 0.312,
...                123.24, 14.46, 9.768, 1.012, 19.24, -15.54, -1.732, 5.412, 65.24,
...                -7.54, -0.032, 2.212, 92.24, -17.54, 23.268, 0.312, 123.24, 14.46,
...                9.76, 1.012, 19.24, -15.54, -1.732, 5.412, 65.24, -7.54, -0.032,
...                2.212, 92.24, -17.54, 23.268, 0.312, 123.24, 14.46, 9.768, 1.012,
...                19.24, -15.54, -1.732, 5.412, 65.24]]*4)
>>> initial_x_h2o = h2o.H2OFrame(list(zip(*initial_x)))
>>> arrests_glrm = H2OGeneralizedLowRankEstimator(k=4,
...                                               transform="demean",
...                                               loss="quadratic",
...                                               gamma_x=0.5,
...                                               gamma_y=0.3,
...                                               init="user",
...                                               user_x=initial_x_h2o,
...                                               recover_svd=True)
>>> arrests_glrm.train(x=arrestsH2O.names, training_frame=arrestsH2O)
>>> arrests_glrm.show()

user_y

User-specified initial Y

Type: H2OFrame.

Examples:

>>> arrestsH2O = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/USArrests.csv")
>>> initial_y = [[5.412,  65.24,  -7.54, -0.032],
...              [2.212,  92.24, -17.54, 23.268],
...              [0.312, 123.24,  14.46,  9.768],
...              [1.012,  19.24, -15.54, -1.732]]
>>> initial_y_h2o = h2o.H2OFrame(list(zip(*initial_y)))
>>> arrests_glrm = H2OGeneralizedLowRankEstimator(k=4,
...                                               transform="demean",
...                                               loss="quadratic",
...                                               gamma_x=0.5,
...                                               gamma_y=0.3,
...                                               init="user",
...                                               user_y=initial_y_h2o,
...                                               recover_svd=True)
>>> arrests_glrm.train(x=arrestsH2O.names, training_frame=arrestsH2O)
>>> arrests_glrm.show()

validation_frame

Id of the validation data frame.

Type: H2OFrame.

Examples:

>>> iris = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/iris/iris_wheader.csv")
>>> iris_glrm = H2OGeneralizedLowRankEstimator(k=3,
...                                            loss="quadratic",
...                                            gamma_x=0.5,
...                                            gamma_y=0.5,
...                                            transform="standardize")
>>> iris_glrm.train(x=iris.names,
...                 training_frame=iris,
...                 validation_frame=iris)
>>> iris_glrm.show()

H2OIsolationForestEstimator

class h2o.estimators.isolation_forest.H2OIsolationForestEstimator(**kwargs)

Bases: h2o.estimators.estimator_base.H2OEstimator

Isolation Forest

Builds an Isolation Forest model. Isolation Forest algorithm samples the training frame and in each iteration builds a tree that partitions the space of the sample observations until it isolates each observation. Length of the path from root to a leaf node of the resulting tree is used to calculate the anomaly score. Anomalies are easier to isolate and their average tree path is expected to be shorter than paths of regular observations.

build_tree_one_node

Run on one node only; no network overhead but fewer cpus used. Suitable for small datasets.

Type: bool (default: False).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> cars_if = H2OIsolationForestEstimator(build_tree_one_node=True,
...                                       seed=1234)
>>> cars_if.train(x=predictors,
...               training_frame=cars)
>>> cars_if.model_performance()

categorical_encoding

Encoding scheme for categorical features

One of: "auto", "enum", "one_hot_internal", "one_hot_explicit", "binary", "eigen", "label_encoder", "sort_by_response", "enum_limited" (default: "auto").

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> encoding = "one_hot_explicit"
>>> airlines_if = H2OIsolationForestEstimator(categorical_encoding=encoding,
...                                           seed=1234)
>>> airlines_if.train(x=predictors,
...                   training_frame=airlines)
>>> airlines_if.model_performance()

col_sample_rate_change_per_level

Relative change of the column sampling rate for every level (must be > 0.0 and <= 2.0)

Type: float (default: 1).

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> airlines_if = H2OIsolationForestEstimator(col_sample_rate_change_per_level=.9,
...                                           seed=1234)
>>> airlines_if.train(x=predictors,
...                   training_frame=airlines)
>>> airlines_if.model_performance()

col_sample_rate_per_tree

Column sample rate per tree (from 0.0 to 1.0)

Type: float (default: 1).

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> airlines_if = H2OIsolationForestEstimator(col_sample_rate_per_tree=.7,
...                                           seed=1234)
>>> airlines_if.train(x=predictors,
...                   training_frame=airlines)
>>> airlines_if.model_performance()

export_checkpoints_dir

Automatically export generated models to this directory.

Type: str.

Examples:

>>> import tempfile
>>> from os import listdir
>>> airlines = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip", destination_frame="air.hex")
>>> predictors = ["DayofMonth", "DayOfWeek"]
>>> checkpoints_dir = tempfile.mkdtemp()
>>> air_if = H2OIsolationForestEstimator(max_depth=3,
...                                      seed=1234,
...                                      export_checkpoints_dir=checkpoints_dir)
>>> air_if.train(x=predictors,
...              training_frame=airlines)
>>> len(listdir(checkpoints_dir))

ignore_const_cols

Ignore constant columns.

Type: bool (default: True).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> cars["const_1"] = 6
>>> cars["const_2"] = 7
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_if = H2OIsolationForestEstimator(seed=1234,
...                                       ignore_const_cols=True)
>>> cars_if.train(x=predictors,
...               training_frame=cars)
>>> cars_if.model_performance()

ignored_columns

Names of columns to ignore for training.

Type: List[str].

max_depth

Maximum tree depth.

Type: int (default: 8).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> cars_if = H2OIsolationForestEstimator(max_depth=2,
...                                       seed=1234)
>>> cars_if.train(x=predictors,
...               training_frame=cars)
>>> cars_if.model_performance()

max_runtime_secs

Maximum allowed runtime in seconds for model training. Use 0 to disable.

Type: float (default: 0).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> cars_if = H2OIsolationForestEstimator(max_runtime_secs=10,
...                                       ntrees=10000,
...                                       max_depth=10,
...                                       seed=1234)
>>> cars_if.train(x=predictors,
...               training_frame=cars)
>>> cars_if.model_performance()

min_rows

Fewest allowed (weighted) observations in a leaf.

Type: float (default: 1).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> cars_if = H2OIsolationForestEstimator(min_rows=16,
...                                       seed=1234)
>>> cars_if.train(x=predictors,
...               training_frame=cars)
>>> cars_if.model_performance()

mtries

Number of variables randomly sampled as candidates at each split. If set to -1, defaults (number of predictors)/3.

Type: int (default: -1).

Examples:

>>> covtype = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/covtype/covtype.20k.data")
>>> predictors = covtype.columns[0:54]
>>> cov_if = H2OIsolationForestEstimator(mtries=30, seed=1234)
>>> cov_if.train(x=predictors,
...              training_frame=covtype)
>>> cov_if.model_performance()

ntrees

Number of trees.

Type: int (default: 50).

Examples:

>>> titanic = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv")
>>> predictors = titanic.columns
>>> tree_num = [20, 50, 80, 110, 140, 170, 200]
>>> label = ["20", "50", "80", "110", "140", "170", "200"]
>>> for key, num in enumerate(tree_num):
...     titanic_if = H2OIsolationForestEstimator(ntrees=num,
...                                              seed=1234)
...     titanic_if.train(x=predictors,
...                      training_frame=titanic) 
...     print(label[key], 'training score', titanic_if.mse(train=True))

sample_rate

Rate of randomly sampled observations used to train each Isolation Forest tree. Needs to be in range from 0.0 to 1.0. If set to -1, sample_rate is disabled and sample_size will be used instead.

Type: float (default: -1).

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> airlines_if = H2OIsolationForestEstimator(sample_rate=.7,
...                                           seed=1234)
>>> airlines_if.train(x=predictors,
...                   training_frame=airlines)
>>> airlines_if.model_performance()

sample_size

Number of randomly sampled observations used to train each Isolation Forest tree. Only one of parameters sample_size and sample_rate should be defined. If sample_rate is defined, sample_size will be ignored.

Type: int (default: 256).

Examples:

>>> train = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/anomaly/ecg_discord_train.csv")
>>> test = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/anomaly/ecg_discord_test.csv")
>>> isofor_model = H2OIsolationForestEstimator(sample_size=5,
...                                            ntrees=7)
>>> isofor_model.train(training_frame=train)
>>> isofor_model.model_performance()
>>> isofor_model.model_performance(test)

score_each_iteration

Whether to score during each iteration of model training.

Type: bool (default: False).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> cars_if = H2OIsolationForestEstimator(score_each_iteration=True,
...                                       ntrees=55,
...                                       seed=1234)
>>> cars_if.train(x=predictors,
...               training_frame=cars)
>>> cars_if.model_performance()

score_tree_interval

Score the model after every so many trees. Disabled if set to 0.

Type: int (default: 0).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> cars_if = H2OIsolationForestEstimator(score_tree_interval=5,
...                                       seed=1234)
>>> cars_if.train(x=predictors,
...               training_frame=cars)
>>> cars_if.model_performance()

seed

Seed for pseudo random number generator (if applicable)

Type: int (default: -1).

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> isofor_w_seed = H2OIsolationForestEstimator(seed=1234) 
>>> isofor_w_seed.train(x=predictors,
...                     training_frame=airlines)
>>> isofor_wo_seed = H2OIsolationForestEstimator()
>>> isofor_wo_seed.train(x=predictors,
...                      training_frame=airlines)
>>> isofor_w_seed.model_performance()
>>> isofor_wo_seed.model_performance()

stopping_metric

Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anonomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client.

One of: "auto", "anomaly_score" (default: "auto").

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> airlines_if = H2OIsolationForestEstimator(stopping_metric="auto",
...                                           stopping_rounds=3,
...                                           stopping_tolerance=1e-2,
...                                           seed=1234)
>>> airlines_if.train(x=predictors,
...                   training_frame=airlines)
>>> airlines_if.model_performance()

stopping_rounds

Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable)

Type: int (default: 0).

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> airlines_if = H2OIsolationForestEstimator(stopping_metric="auto",
...                                           stopping_rounds=3,
...                                           stopping_tolerance=1e-2,
...                                           seed=1234)
>>> airlines_if.train(x=predictors,
...                   training_frame=airlines)
>>> airlines_if.model_performance()

stopping_tolerance

Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much)

Type: float (default: 0.01).

Examples:

>>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
>>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier",
...               "DayOfWeek", "Month", "Distance", "FlightNum"]
>>> airlines_if = H2OIsolationForestEstimator(stopping_metric="auto",
...                                           stopping_rounds=3,
...                                           stopping_tolerance=1e-2,
...                                           seed=1234)
>>> airlines_if.train(x=predictors,
...                   training_frame=airlines)
>>> airlines_if.model_performance()

training_frame

Id of the training data frame.

Type: H2OFrame.

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> cars_if = H2OIsolationForestEstimator(seed=1234)
>>> cars_if.train(x=predictors,
...               training_frame=cars)
>>> cars_if.model_performance()

H2OKMeansEstimator

class h2o.estimators.kmeans.H2OKMeansEstimator(**kwargs)

Bases: h2o.estimators.estimator_base.H2OEstimator

K-means

Performs k-means clustering on an H2O dataset.

categorical_encoding

Encoding scheme for categorical features

One of: "auto", "enum", "one_hot_internal", "one_hot_explicit", "binary", "eigen", "label_encoder", "sort_by_response", "enum_limited" (default: "auto").

Examples:

>>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv")
>>> predictors = ["AGE", "RACE", "DPROS", "DCAPS", "PSA", "VOL", "GLEASON"]
>>> train, valid = prostate.split_frame(ratios=[.8], seed=1234)
>>> encoding = "one_hot_explicit"
>>> pros_km = H2OKMeansEstimator(categorical_encoding=encoding,
...                              seed=1234)
>>> pros_km.train(x=predictors,
...               training_frame=train,
...               validation_frame=valid)
>>> pros_km.scoring_history()

cluster_size_constraints

An array specifying the minimum number of points that should be in each cluster. The length of the constraints array has to be the same as the number of clusters.

Type: List[int].

Examples:

>>> iris_h2o = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris.csv")
>>> k=3
>>> start_points = h2o.H2OFrame(
...         [[4.9, 3.0, 1.4, 0.2],
...          [5.6, 2.5, 3.9, 1.1],
...          [6.5, 3.0, 5.2, 2.0]])
>>> kmm = H2OKMeansEstimator(k=k,
...                          user_points=start_points,
...                          standardize=True,
...                          cluster_size_constraints=[2, 5, 8],
...                          score_each_iteration=True)
>>> kmm.train(x=list(range(7)), training_frame=iris_h2o)
>>> kmm.scoring_history()

estimate_k

Whether to estimate the number of clusters (<=k) iteratively and deterministically.

Type: bool (default: False).

Examples:

>>> iris = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_wheader.csv")
>>> iris['class'] = iris['class'].asfactor()
>>> predictors = iris.columns[:-1]
>>> train, valid = iris.split_frame(ratios=[.8], seed=1234)
>>> iris_kmeans = H2OKMeansEstimator(k=10,
...                                  estimate_k=True,
...                                  standardize=False,
...                                  seed=1234)
>>> iris_kmeans.train(x=predictors,
...                   training_frame=train,
...                   validation_frame=valid)
>>> iris_kmeans.scoring_history()

export_checkpoints_dir

Automatically export generated models to this directory.

Type: str.

Examples:

>>> import tempfile
>>> from os import listdir
>>> airlines = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip", destination_frame="air.hex")
>>> predictors = ["DayofMonth", "DayOfWeek"]
>>> checkpoints_dir = tempfile.mkdtemp()
>>> air_km = H2OKMeansEstimator(export_checkpoints_dir=checkpoints_dir,
...                             seed=1234)
>>> air_km.train(x=predictors, training_frame=airlines)
>>> len(listdir(checkpoints_dir))

fold_assignment

Cross-validation fold assignment scheme, if fold_column is not specified. The ‘Stratified’ option will stratify the folds based on the response variable, for classification problems.

One of: "auto", "random", "modulo", "stratified" (default: "auto").

Examples:

>>> ozone = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/ozone.csv")
>>> predictors = ["radiation","temperature","wind"]
>>> train, valid = ozone.split_frame(ratios=[.8], seed=1234)
>>> ozone_km = H2OKMeansEstimator(fold_assignment="Random",
...                               nfolds=5,
...                               seed=1234)
>>> ozone_km.train(x=predictors,
...                training_frame=train,
...                validation_frame=valid)
>>> ozone_km.scoring_history()

fold_column

Column with cross-validation fold index assignment per observation.

Type: str.

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> fold_numbers = cars.kfold_column(n_folds=5, seed=1234)
>>> fold_numbers.set_names(["fold_numbers"])
>>> cars = cars.cbind(fold_numbers)
>>> print(cars['fold_numbers'])
>>> cars_km = H2OKMeansEstimator(seed=1234)
>>> cars_km.train(x=predictors,
...               training_frame=cars,
...               fold_column="fold_numbers")
>>> cars_km.scoring_history()

ignore_const_cols

Ignore constant columns.

Type: bool (default: True).

Examples:

>>> cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")
>>> predictors = ["displacement","power","weight","acceleration","year"]
>>> cars["const_1"] = 6
>>> cars["const_2"] = 7
>>> train, valid = cars.split_frame(ratios=[.8], seed=1234)
>>> cars_km = H2OKMeansEstimator(ignore_const_cols=True,
...                              seed=1234)
>>> cars_km.train(x=predictors,
...               training_frame=train,
...               validation_frame=valid)
>>> cars_km.scoring_history()

ignored_columns

Names of columns to ignore for training.

Type: List[str].

init

Initialization mode

One of: "random", "plus_plus", "furthest", "user" (default: "furthest").

Examples:

>>> seeds = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/flow_examples/seeds_dataset.txt")
>>> predictors = seeds.columns[0:7]
>>> train, valid = seeds.split_frame(ratios=[.8], seed=1234)
>>> seeds_km = H2OKMeansEstimator(k=3,
...                               init='Furthest',
...                               seed=1234)
>>> seeds_km.train(x=predictors,
...                training_frame=train,
...                validation_frame= valid)
>>> seeds_km.scoring_history()

k

The max. number of clusters. If estimate_k is disabled, the model will find k centroids, otherwise it will find up to k centroids.

Type: int (default: 1).

Examples:

>>> seeds = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/flow_examples/seeds_dataset.txt")
>>> predictors = seeds.columns[0:7]
>>> train, valid = seeds.split_frame(ratios=[.8], seed=1234)
>>> seeds_km = H2OKMeansEstimator(k=3, seed=1234)
>>> seeds_km.train(x=predictors,
...                training_frame=train,
...                validation_frame=valid)
>>> seeds_km.scoring_history()

keep_cross_validation_fold_assignment

Whether to keep the cross-validation fold assignment.

Type: bool (default: False).

Examples:

>>> ozone = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/ozone.csv")
>>> predictors = ["radiation","temperature","wind"]
>>> train, valid = ozone.split_frame(ratios=[.8], seed=1234)
>>> ozone_km = H2OKMeansEstimator(keep_cross_validation_fold_assignment=True,
...                               nfolds=5,
...                               seed=1234)
>>> ozone_km.train(x=predictors,
...                training_frame=train)
>>> ozone_km.scoring_history()

keep_cross_validation_models

Whether to keep the cross-validation models.

Type: bool (default: True).

Examples:

>>> ozone = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/ozone.csv")
>>> predictors = ["radiation","temperature","wind"]
>>> train, valid = ozone.split_frame(ratios=[.8], seed=1234)
>>> ozone_km = H2OKMeansEstimator(keep_cross_validation_models=True,
...                               nfolds=5,
...                               seed=1234)
>>> ozone_km.train(x=predictors,
...                training_frame=train,
...                validation_frame=valid)
>>> ozone_km.scoring_history()

keep_cross_validation_predictions

Whether to keep the predictions of the cross-validation models.

Type: bool (default: False).

Examples:

>>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv")
>>> predictors = ["AGE", "RACE", "DPROS", "DCAPS",
...               "PSA", "VOL", "GLEASON"]
>>> train, valid = prostate.split_frame(ratios=[.8], seed=1234)
>>> pros_km = H2OKMeansEstimator(keep_cross_validation_predictions=True,
...                              nfolds=5,
...                              seed=1234)
>>> pros_km.train(x=predictors,
...               training_frame=train,
...               validation_frame=valid)
>>> pros_km.scoring_history()

max_iterations

Maximum training iterations (if estimate_k is enabled, then this is for each inner Lloyds iteration)

Type: int (default: 10).

Examples:

>>> benign = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/benign.csv")
>>> predictors = ["AGMT","FNDX","HIGD","DEG","CHK",
...               "AGP1","AGMN","LIV","AGLP"]
>>> train, valid = benign.split_frame(ratios=[.8], seed=1234)
>>> benign_km = H2OKMeansEstimator(max_iterations=50)
>>> benign_km.train(x=predictors,
...                 training_frame=train,
...                 validation_frame=valid)
>>> benign_km.scoring_history()

max_runtime_secs

Maximum allowed runtime in seconds for model training. Use 0 to disable.

Type: float (default: 0).

Examples:

>>> benign = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/benign.csv")
>>> predictors = ["AGMT","FNDX","HIGD","DEG","CHK",
...               "AGP1","AGMN","LIV","AGLP"]
>>> train, valid = benign.split_frame(ratios=[.8], seed=1234)
>>> benign_km = H2OKMeansEstimator(max_runtime_secs=10,
...                                seed=1234)
>>> benign_km.train(x=predictors,
...                 training_frame=train,
...                 validation_frame=valid)
>>> benign_km.scoring_history()

nfolds

Number of folds for K-fold cross-validation (0 to disable or >= 2).

Type: int (default: 0).

Examples:

>>> benign = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/benign.csv")
>>> predictors = ["AGMT","FNDX","HIGD","DEG","CHK",
...               "AGP1","AGMN","LIV","AGLP"]
>>> train, valid = benign.split_frame(ratios=[.8], seed=1234)
>>> benign_km = H2OKMeansEstimator(nfolds=5, seed=1234)
>>> benign_km.train(x=predictors,
...                 training_frame=train,
...                 validation_frame=valid)
>>> benign_km.scoring_history()

score_each_iteration

Whether to score during each iteration of model training.

Type: bool (default: False).

Examples:

>>> benign = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/benign.csv")
>>> predictors = ["AGMT","FNDX","HIGD","DEG","CHK",
...               "AGP1","AGMN","LIV","AGLP"]
>>> train, valid = benign.split_frame(ratios=[.8], seed=1234)
>>> benign_km = H2OKMeansEstimator(score_each_iteration=True,
...                                seed=1234)
>>> benign_km.train(x=predictors,
...                 training_frame=train,
...                 validation_frame=valid)
>>> benign_km.scoring_history()

seed

RNG Seed

Type: int (default: -1).

Examples:

>>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv")
>>> predictors = ["AGE", "RACE", "DPROS", "DCAPS", "PSA", "VOL", "GLEASON"]
>>> train, valid = prostate.split_frame(ratios=[.8], seed=1234)
>>> pros_w_seed = H2OKMeansEstimator(seed=1234)
>>> pros_w_seed.train(x=predictors,
...                   training_frame=train,
...                   validation_frame=valid)
>>> pros_wo_seed = H2OKMeansEstimator()
>>> pros_wo_seed.train(x=predictors,
...                    training_frame=train,
...                    validation_frame=valid)
>>> pros_w_seed.scoring_history()
>>> pros_wo_seed.scoring_history()

standardize

Standardize columns before computing distances

Type: bool (default: True).

Examples:

>>> boston = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv")
>>> predictors = boston.columns[:-1]
>>> boston['chas'] = boston['chas'].asfactor()
>>> train, valid = boston.split_frame(ratios=[.8])
>>> boston_km = H2OKMeansEstimator(standardize=True)
>>> boston_km.train(x=predictors,
...                 training_frame=train,
...                 validation_frame=valid)
>>> boston_km.scoring_history()

training_frame

Id of the training data frame.

Type: H2OFrame.

Examples:

>>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv")
>>> predictors = ["AGE", "RACE", "DPROS", "DCAPS",
...               "PSA", "VOL", "GLEASON"]
>>> train, valid = prostate.split_frame(ratios=[.8], seed=1234)
>>> pros_km = H2OKMeansEstimator(seed=1234)
>>> pros_km.train(x=predictors,
...               training_frame=train,
...               validation_frame=valid)
>>> pros_km.scoring_history()

user_points

This option allows you to specify a dataframe, where each row represents an initial cluster center. The user- specified points must have the same number of columns as the training observations. The number of rows must equal the number of clusters

Type: H2OFrame.

Examples:

>>> iris = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_wheader.csv")
>>> iris['class'] = iris['class'].asfactor()
>>> predictors = iris.columns[:-1]
>>> train, valid = iris.split_frame(ratios=[.8], seed=1234)
>>> point1 = [4.9,3.0,1.4,0.2]
>>> point2 = [5.6,2.5,3.9,1.1]
>>> point3 = [6.5,3.0,5.2,2.0]
>>> points = h2o.H2OFrame([point1, point2, point3])
>>> iris_km = H2OKMeansEstimator(k=3,
...                              user_points=points,
...                              seed=1234)
>>> iris_km.train(x=predictors,
...               training_frame=iris,
...               validation_frame=valid)
>>> iris_kmeans.tot_withinss(valid=True)

validation_frame

Id of the validation data frame.

Type: H2OFrame.

Examples:

>>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv")
>>> predictors = ["AGE", "RACE", "DPROS", "DCAPS",
...               "PSA", "VOL", "GLEASON"]
>>> train, valid = prostate.split_frame(ratios=[.8], seed=1234)
>>> pros_km = H2OKMeansEstimator(seed=1234)
>>> pros_km.train(x=predictors,
...               training_frame=train,
...               validation_frame=valid)
>>> pros_km.scoring_history()

H2OPrincipalComponentAnalysisEstimator

class h2o.estimators.pca.H2OPrincipalComponentAnalysisEstimator(**kwargs)

Bases: h2o.estimators.estimator_base.H2OEstimator

Principal Components Analysis

compute_metrics

Whether to compute metrics on the training data

Type: bool (default: True).

Examples:

>>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv.zip")
>>> prostate['CAPSULE'] = prostate['CAPSULE'].asfactor()
>>> prostate['RACE'] = prostate['RACE'].asfactor()
>>> prostate['DCAPS'] = prostate['DCAPS'].asfactor()
>>> prostate['DPROS'] = prostate['DPROS'].asfactor()
>>> pros_pca = H2OPrincipalComponentAnalysisEstimator(compute_metrics=False)
>>> pros_pca.train(x=prostate.names, training_frame=prostate)
>>> pros_pca.show()

export_checkpoints_dir

Automatically export generated models to this directory.

Type: str.

Examples:

>>> import tempfile
>>> from os import listdir
>>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv.zip")
>>> prostate['CAPSULE'] = prostate['CAPSULE'].asfactor()
>>> prostate['RACE'] = prostate['RACE'].asfactor()
>>> prostate['DCAPS'] = prostate['DCAPS'].asfactor()
>>> prostate['DPROS'] = prostate['DPROS'].asfactor()
>>> checkpoints_dir = tempfile.mkdtemp()
>>> pros_pca = H2OPrincipalComponentAnalysisEstimator(impute_missing=True,
...                                                   export_checkpoints_dir=checkpoints_dir)
>>> pros_pca.train(x=prostate.names, training_frame=prostate)
>>> len(listdir(checkpoints_dir))

ignore_const_cols

Ignore constant columns.

Type: bool (default: True).

Examples:

>>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv.zip")
>>> prostate['CAPSULE'] = prostate['CAPSULE'].asfactor()
>>> prostate['RACE'] = prostate['RACE'].asfactor()
>>> prostate['DCAPS'] = prostate['DCAPS'].asfactor()
>>> prostate['DPROS'] = prostate['DPROS'].asfactor()
>>> pros_pca = H2OPrincipalComponentAnalysisEstimator(ignore_const_cols=False)
>>> pros_pca.train(x=prostate.names, training_frame=prostate)
>>> pros_pca.show()

ignored_columns

Names of columns to ignore for training.

Type: List[str].

impute_missing

Whether to impute missing entries with the column mean

Type: bool (default: False).

Examples:

>>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv.zip")
>>> prostate['CAPSULE'] = prostate['CAPSULE'].asfactor()
>>> prostate['RACE'] = prostate['RACE'].asfactor()
>>> prostate['DCAPS'] = prostate['DCAPS'].asfactor()
>>> prostate['DPROS'] = prostate['DPROS'].asfactor()
>>> pros_pca = H2OPrincipalComponentAnalysisEstimator(impute_missing=True)
>>> pros_pca.train(x=prostate.names, training_frame=prostate)
>>> pros_pca.show()

init_for_pipeline()

Returns H2OPCA object which implements fit and transform method to be used in sklearn.Pipeline properly. All parameters defined in self.__params, should be input parameters in H2OPCA.__init__ method.

Returns:H2OPCA object Examples:

>>> from sklearn.pipeline import Pipeline
>>> from h2o.transforms.preprocessing import H2OScaler
>>> from h2o.estimators import H2ORandomForestEstimator
>>> iris = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_wheader.csv")
>>> pipe = Pipeline([("standardize", H2OScaler()),
...                  ("pca", H2OPrincipalComponentAnalysisEstimator(k=2).init_for_pipeline()),
...                  ("rf", H2ORandomForestEstimator(seed=42,ntrees=5))])
>>> pipe.fit(iris[:4], iris[4])

k

Rank of matrix approximation

Type: int (default: 1).

Examples:

>>> data = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/SDSS_quasar.txt.zip")
>>> data_pca = H2OPrincipalComponentAnalysisEstimator(k=-1,
...                                                   transform="standardize",
...                                                   pca_method="power",
...                                                   impute_missing=True,
...                                                   max_iterations=800)
>>> data_pca.train(x=data.names, training_frame=data)
>>> data_pca.show()

max_iterations

Maximum training iterations

Type: int (default: 1000).

Examples:

>>> data = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/SDSS_quasar.txt.zip")
>>> data_pca = H2OPrincipalComponentAnalysisEstimator(k=-1,
...                                                   transform="standardize",
...                                                   pca_method="power",
...                                                   impute_missing=True,
...                                                   max_iterations=800)
>>> data_pca.train(x=data.names, training_frame=data)
>>> data_pca.show()

max_runtime_secs

Maximum allowed runtime in seconds for model training. Use 0 to disable.

Type: float (default: 0).

Examples:

>>> data = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/SDSS_quasar.txt.zip")
>>> data_pca = H2OPrincipalComponentAnalysisEstimator(k=-1,
...                                                   transform="standardize",
...                                                   pca_method="power",
...                                                   impute_missing=True,
...                                                   max_iterations=800
...                                                   max_runtime_secs=15)
>>> data_pca.train(x=data.names, training_frame=data)
>>> data_pca.show()

pca_impl

Specify the implementation to use for computing PCA (via SVD or EVD): MTJ_EVD_DENSEMATRIX - eigenvalue decompositions for dense matrix using MTJ; MTJ_EVD_SYMMMATRIX - eigenvalue decompositions for symmetric matrix using MTJ; MTJ_SVD_DENSEMATRIX - singular-value decompositions for dense matrix using MTJ; JAMA - eigenvalue decompositions for dense matrix using JAMA. References: JAMA - http://math.nist.gov/javanumerics/jama/; MTJ - https://github.com/fommil/matrix-toolkits-java/

One of: "mtj_evd_densematrix", "mtj_evd_symmmatrix", "mtj_svd_densematrix", "jama".

Examples:

>>> data = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/SDSS_quasar.txt.zip")
>>> data_pca = H2OPrincipalComponentAnalysisEstimator(k=3,
...                                                   pca_impl="jama",
...                                                   impute_missing=True,
...                                                   max_iterations=1200)
>>> data_pca.train(x=data.names, training_frame=data)
>>> data_pca.show()

pca_method

Specify the algorithm to use for computing the principal components: GramSVD - uses a distributed computation of the Gram matrix, followed by a local SVD; Power - computes the SVD using the power iteration method (experimental); Randomized - uses randomized subspace iteration method; GLRM - fits a generalized low-rank model with L2 loss function and no regularization and solves for the SVD using local matrix algebra (experimental)

One of: "gram_s_v_d", "power", "randomized", "glrm" (default: "gram_s_v_d").

Examples:

>>> data = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/SDSS_quasar.txt.zip")
>>> data_pca = H2OPrincipalComponentAnalysisEstimator(k=-1,
...                                                   transform="standardize",
...                                                   pca_method="power",
...                                                   impute_missing=True,
...                                                   max_iterations=800)
>>> data_pca.train(x=data.names, training_frame=data)
>>> data_pca.show()

score_each_iteration

Whether to score during each iteration of model training.

Type: bool (default: False).

Examples:

>>> data = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/SDSS_quasar.txt.zip")
>>> data_pca = H2OPrincipalComponentAnalysisEstimator(k=3,
...                                                   score_each_iteration=True,
...                                                   seed=1234,
...                                                   impute_missing=True)
>>> data_pca.train(x=data.names, training_frame=data)
>>> data_pca.show()

seed

RNG seed for initialization

Type: int (default: -1).

Examples:

>>> data = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/SDSS_quasar.txt.zip")
>>> data_pca = H2OPrincipalComponentAnalysisEstimator(k=3,
...                                                   seed=1234,
...                                                   impute_missing=True)
>>> data_pca.train(x=data.names, training_frame=data)
>>> data_pca.show()

training_frame

Id of the training data frame.

Type: H2OFrame.

Examples:

>>> data = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/SDSS_quasar.txt.zip")
>>> data_pca = H2OPrincipalComponentAnalysisEstimator()
>>> data_pca.train(x=data.names, training_frame=data)
>>> data_pca.show()

transform

Transformation of training data

One of: "none", "standardize", "normalize", "demean", "descale" (default: "none").

Examples:

>>> data = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/SDSS_quasar.txt.zip")
>>> data_pca = H2OPrincipalComponentAnalysisEstimator(k=-1,
...                                                   transform="standardize",
...                                                   pca_method="power",
...                                                   impute_missing=True,
...                                                   max_iterations=800)
>>> data_pca.train(x=data.names, training_frame=data)
>>> data_pca.show()

use_all_factor_levels

Whether first factor level is included in each categorical expansion

Type: bool (default: False).

Examples:

>>> data = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/SDSS_quasar.txt.zip")
>>> data_pca = H2OPrincipalComponentAnalysisEstimator(k=3,
...                                                   use_all_factor_levels=True,
...                                                   seed=1234)
>>> data_pca.train(x=data.names, training_frame=data)
>>> data_pca.show()

validation_frame

Id of the validation data frame.

Type: H2OFrame.

Examples:

>>> data = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/SDSS_quasar.txt.zip")
>>> train, valid = data.split_frame(ratios=[.8], seed=1234)
>>> model_pca = H2OPrincipalComponentAnalysisEstimator(impute_missing=True)
>>> model_pca.train(x=data.names,
...                training_frame=train,
...                validation_frame=valid)
>>> model_pca.show()

Miscellaneous

automl

h2o.automl.get_automl(project_name)

Retrieve information about an AutoML instance.

Parameters:project_name (str) – A string indicating the project_name of the automl instance to retrieve. Returns:A dictionary containing the project_name, leader model, leaderboard, event_log.

h2o.automl.get_leaderboard(aml, extra_columns=None)

Retrieve the leaderboard from the AutoML instance. Contrary to the default leaderboard attached to the automl instance, this one can return columns other than the metrics. :param H2OAutoML aml: the instance for which to return the leaderboard. :param extra_columns: a string or a list of string specifying which optional columns should be added to the leaderboard. Defaults to None.

Currently supported extensions are: - ‘ALL’: adds all columns below. - ‘training_time_ms’: column providing the training time of each model in milliseconds (doesn’t include the training of cross validation models). - ‘predict_time_per_row_ms`: column providing the average prediction time by the model for a single row.

Returns:An H2OFrame representing the leaderboard. Examples:

>>> aml = H2OAutoML(max_runtime_secs=30)
>>> aml.train(y=y, training_frame=train)
>>> lb_all = h2o.automl.get_leaderboard(aml, 'ALL')
>>> lb_custom = h2o.automl.get_leaderboard(aml, ['predict_time_per_row_ms', 'training_time_ms'])
>>> lb_custom_sorted = lb_custom.sort(by='predict_time_per_row_ms')

H2OAutoML

class h2o.automl.H2OAutoML(nfolds=5, balance_classes=False, class_sampling_factors=None, max_after_balance_size=5.0, max_runtime_secs=None, max_runtime_secs_per_model=None, max_models=None, stopping_metric='AUTO', stopping_tolerance=None, stopping_rounds=3, seed=None, project_name=None, exclude_algos=None, include_algos=None, modeling_plan=None, monotone_constraints=None, algo_parameters=None, keep_cross_validation_predictions=False, keep_cross_validation_models=False, keep_cross_validation_fold_assignment=False, sort_metric='AUTO', export_checkpoints_dir=None, verbosity='warn')

Bases: h2o.automl._base.H2OAutoMLBaseMixin, h2o.base.Keyed

Automatic Machine Learning

The Automatic Machine Learning (AutoML) function automates the supervised machine learning model training process. The current version of AutoML trains and cross-validates a Random Forest (DRF), an Extremely-Randomized Forest (DRF/XRT), a random grid of Generalized Linear Models (GLM) a random grid of XGBoost (XGBoost), a random grid of Gradient Boosting Machines (GBM), a random grid of Deep Neural Nets (DeepLearning), and 2 Stacked Ensembles, one of all the models, and one of only the best models of each kind.

Examples:

>>> import h2o
>>> from h2o.automl import H2OAutoML
>>> h2o.init()
>>> # Import a sample binary outcome train/test set into H2O
>>> train = h2o.import_file("https://s3.amazonaws.com/erin-data/higgs/higgs_train_10k.csv")
>>> test = h2o.import_file("https://s3.amazonaws.com/erin-data/higgs/higgs_test_5k.csv")
>>> # Identify the response and set of predictors
>>> y = "response"
>>> x = list(train.columns)  #if x is defined as all columns except the response, then x is not required
>>> x.remove(y)
>>> # For binary classification, response should be a factor
>>> train[y] = train[y].asfactor()
>>> test[y] = test[y].asfactor()
>>> # Run AutoML for 30 seconds
>>> aml = H2OAutoML(max_runtime_secs = 30)
>>> aml.train(x = x, y = y, training_frame = train)
>>> # Print Leaderboard (ranked by xval metrics)
>>> aml.leaderboard
>>> # (Optional) Evaluate performance on a test set
>>> perf = aml.leader.model_performance(test)
>>> perf.auc()

modeling_steps

expose the modeling steps effectively used by the AutoML run. This executed plan can be directly reinjected as the modeling_plan property of a new AutoML instance

to improve reproducibility across AutoML versions.

Returns:a list of dictionaries representing the effective modeling plan.

train(x=None, y=None, training_frame=None, fold_column=None, weights_column=None, validation_frame=None, leaderboard_frame=None, blending_frame=None)

Begins an AutoML task, a background task that automatically builds a number of models with various algorithms and tracks their performance in a leaderboard. At any point in the process you may use H2O’s performance or prediction functions on the resulting models.

Parameters:

• x – A list of column names or indices indicating the predictor columns.
• y – An index or a column name indicating the response column.
• fold_column – The name or index of the column in training_frame that holds per-row fold assignments.
• weights_column – The name or index of the column in training_frame that holds per-row weights.
• training_frame – The H2OFrame having the columns indicated by x and y (as well as any additional columns specified by fold_column or weights_column).
• validation_frame – H2OFrame with validation data. This argument is ignored unless the user sets nfolds = 0. If cross-validation is turned off, then a validation frame can be specified and used for early stopping of individual models and early stopping of the grid searches. By default and when nfolds > 1, cross-validation metrics will be used for early stopping and thus validation_frame will be ignored.
• leaderboard_frame – H2OFrame with test data for scoring the leaderboard. This is optional and if this is set to None (the default), then cross-validation metrics will be used to generate the leaderboard rankings instead.
• blending_frame – H2OFrame used to train the the metalearning algorithm in Stacked Ensembles (instead of relying on cross-validated predicted values). This is optional, but when provided, it is also recommended to disable cross validation by setting nfolds=0 and to provide a leaderboard frame for scoring purposes.

Returns:

An H2OAutoML object.

Examples:

>>> # Set up an H2OAutoML object
>>> aml = H2OAutoML(max_runtime_secs=30)
>>> # Launch an AutoML run
>>> aml.train(y=y, training_frame=train)

H2OEstimator

class h2o.estimators.estimator_base.H2OEstimator(*args, **kwargs)

Bases: h2o.model.model_base.ModelBase

Base class for H2O Estimators.

H2O Estimators implement the following methods for model construction:

• start() - Top-level user-facing API for asynchronous model build
• join() - Top-level user-facing API for blocking on async model build
• train() - Top-level user-facing API for model building.
• fit() - Used by scikit-learn.

Because H2OEstimator instances are instances of ModelBase, these objects can use the H2O model API.

convert_H2OXGBoostParams_2_XGBoostParams()

In order to use convert_H2OXGBoostParams_2_XGBoostParams and convert_H2OFrame_2_DMatrix, you must import the following toolboxes: xgboost, pandas, numpy and scipy.sparse.

Given an H2OXGBoost model, this method will generate the corresponding parameters that should be used by native XGBoost in order to give exactly the same result, assuming that the same dataset (derived from h2oFrame) is used to train the native XGBoost model.

Follow the steps below to compare H2OXGBoost and native XGBoost:

1. Train the H2OXGBoost model with H2OFrame trainFile and generate a prediction:

• h2oModelD = H2OXGBoostEstimator(**h2oParamsD) # parameters specified as a dict()
• h2oModelD.train(x=myX, y=y, training_frame=trainFile) # train with H2OFrame trainFile
• h2oPredict = h2oPredictD = h2oModelD.predict(trainFile)

2. Derive the DMatrix from H2OFrame:

• nativeDMatrix = trainFile.convert_H2OFrame_2_DMatrix(myX, y, h2oModelD)

3. Derive the parameters for native XGBoost:

• nativeParams = h2oModelD.convert_H2OXGBoostParams_2_XGBoostParams()

4. Train your native XGBoost model and generate a prediction:

• nativeModel = xgb.train(params=nativeParams[0], dtrain=nativeDMatrix, num_boost_round=nativeParams[1])
• nativePredict = nativeModel.predict(data=nativeDMatrix, ntree_limit=nativeParams[1]

5. Compare the predictions h2oPredict from H2OXGBoost, nativePredict from native XGBoost.

Returns:nativeParams, num_boost_round

fit(X, y=None, **params)

Fit an H2O model as part of a scikit-learn pipeline or grid search.

A warning will be issued if a caller other than sklearn attempts to use this method.

Parameters:

• X (H2OFrame) – An H2OFrame consisting of the predictor variables.
• y (H2OFrame) – An H2OFrame consisting of the response variable.
• params – Extra arguments.

Returns:

The current instance of H2OEstimator for method chaining.

get_params(deep=True)

Obtain parameters for this estimator.

Used primarily for sklearn Pipelines and sklearn grid search.

Parameters:deep – If True, return parameters of all sub-objects that are estimators. Returns:A dict of parameters

join()

Wait until job’s completion.

set_params(**parms)

Used by sklearn for updating parameters during grid search.

Parameters:parms – A dictionary of parameters that will be set on this model. Returns:self, the current estimator object with the parameters all set as desired.

start(x, y=None, training_frame=None, offset_column=None, fold_column=None, weights_column=None, validation_frame=None, **params)

Train the model asynchronously (to block for results call join()).

Parameters:

• x – A list of column names or indices indicating the predictor columns.
• y – An index or a column name indicating the response column.
• training_frame (H2OFrame) – The H2OFrame having the columns indicated by x and y (as well as any additional columns specified by fold, offset, and weights).
• offset_column – The name or index of the column in training_frame that holds the offsets.
• fold_column – The name or index of the column in training_frame that holds the per-row fold assignments.
• weights_column – The name or index of the column in training_frame that holds the per-row weights.
• validation_frame – H2OFrame with validation data to be scored on while training.

train(x=None, y=None, training_frame=None, offset_column=None, fold_column=None, weights_column=None, validation_frame=None, max_runtime_secs=None, ignored_columns=None, model_id=None, verbose=False)

Train the H2O model.

Parameters:

• x – A list of column names or indices indicating the predictor columns.
• y – An index or a column name indicating the response column.
• training_frame (H2OFrame) – The H2OFrame having the columns indicated by x and y (as well as any additional columns specified by fold, offset, and weights).
• offset_column – The name or index of the column in training_frame that holds the offsets.
• fold_column – The name or index of the column in training_frame that holds the per-row fold assignments.
• weights_column – The name or index of the column in training_frame that holds the per-row weights.
• validation_frame – H2OFrame with validation data to be scored on while training.
• max_runtime_secs (float) – Maximum allowed runtime in seconds for model training. Use 0 to disable.
• verbose (bool) – Print scoring history to stdout. Defaults to False.

H2OSingularValueDecompositionEstimator

class h2o.estimators.svd.H2OSingularValueDecompositionEstimator(**kwargs)

Bases: h2o.estimators.estimator_base.H2OEstimator

Singular Value Decomposition

export_checkpoints_dir

Automatically export generated models to this directory.

Type: str.

Examples:

>>> import tempfile
>>> from os import listdir
>>> arrests = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/USArrests.csv")
>>> checkpoints_dir = tempfile.mkdtemp()
>>> fit_h2o = H2OSingularValueDecompositionEstimator(export_checkpoints_dir=checkpoints_dir,
...                                                  seed=-5)
>>> fit_h2o.train(x=list(range(4)), training_frame=arrests)
>>> len(listdir(checkpoints_dir))

ignore_const_cols

Ignore constant columns.

Type: bool (default: True).

Examples:

>>> arrests = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/USArrests.csv")
>>> fit_h2o = H2OSingularValueDecompositionEstimator(ignore_const_cols=False,
...                                                  nv=4)
>>> fit_h2o.train(x=list(range(4)), training_frame=arrests)
>>> fit_h2o

ignored_columns

Names of columns to ignore for training.

Type: List[str].

init_for_pipeline()

Returns H2OSVD object which implements fit and transform method to be used in sklearn.Pipeline properly. All parameters defined in self.__params, should be input parameters in H2OSVD.__init__ method.

Returns:H2OSVD object Examples:

>>> from h2o.transforms.preprocessing import H2OScaler
>>> from h2o.estimators import H2ORandomForestEstimator
>>> from h2o.estimators import H2OSingularValueDecompositionEstimator
>>> from sklearn.pipeline import Pipeline
>>> arrests = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/USArrests.csv")
>>> pipe = Pipeline([("standardize", H2OScaler()),
...                  ("svd", H2OSingularValueDecompositionEstimator(nv=3).init_for_pipeline()),
...                  ("rf", H2ORandomForestEstimator(seed=42,ntrees=50))])
>>> pipe.fit(arrests[1:], arrests[0])

keep_u

Save left singular vectors?

Type: bool (default: True).

Examples:

>>> arrests = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/USArrests.csv")
>>> fit_h2o = H2OSingularValueDecompositionEstimator(keep_u=False)
>>> fit_h2o.train(x=list(range(4)), training_frame=arrests)
>>> fit_h2o

max_iterations

Maximum iterations

Type: int (default: 1000).

Examples:

>>> arrests = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/USArrests.csv")
>>> fit_h2o = H2OSingularValueDecompositionEstimator(nv=4,
...                                                  transform="standardize",
...                                                  max_iterations=2000)
>>> fit_h2o.train(x=list(range(4)), training_frame=arrests)
>>> fit_h2o

max_runtime_secs

Maximum allowed runtime in seconds for model training. Use 0 to disable.

Type: float (default: 0).

Examples:

>>> arrests = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/USArrests.csv")
>>> fit_h2o = H2OSingularValueDecompositionEstimator(nv=4,
...                                                  transform="standardize",
...                                                  max_runtime_secs=25)
>>> fit_h2o.train(x=list(range(4)), training_frame=arrests)
>>> fit_h2o

nv

Number of right singular vectors

Type: int (default: 1).

Examples:

>>> arrests = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/USArrests.csv")
>>> fit_h2o = H2OSingularValueDecompositionEstimator(nv=4,
...                                                  transform="standardize",
...                                                  max_iterations=2000)
>>> fit_h2o.train(x=list(range(4)), training_frame=arrests)
>>> fit_h2o

score_each_iteration

Whether to score during each iteration of model training.

Type: bool (default: False).

Examples:

>>> arrests = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/USArrests.csv")
>>> fit_h2o = H2OSingularValueDecompositionEstimator(nv=4,
...                                                  score_each_iteration=True)
>>> fit_h2o.train(x=list(range(4)), training_frame=arrests)
>>> fit_h2o

seed

RNG seed for k-means++ initialization

Type: int (default: -1).

Examples:

>>> arrests = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/USArrests.csv")
>>> fit_h2o = H2OSingularValueDecompositionEstimator(nv=4, seed=-3)
>>> fit_h2o.train(x=list(range(4)), training_frame=arrests)
>>> fit_h2o

svd_method

Method for computing SVD (Caution: Randomized is currently experimental and unstable)

One of: "gram_s_v_d", "power", "randomized" (default: "gram_s_v_d").

Examples:

>>> arrests = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/USArrests.csv")
>>> fit_h2o = H2OSingularValueDecompositionEstimator(svd_method="power")
>>> fit_h2o.train(x=list(range(4)), training_frame=arrests)
>>> fit_h2o

training_frame

Id of the training data frame.

Type: H2OFrame.

Examples:

>>> arrests = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/USArrests.csv")
>>> fit_h2o = H2OSingularValueDecompositionEstimator()
>>> fit_h2o.train(x=list(range(4)), training_frame=arrests)
>>> fit_h2o

transform

Transformation of training data

One of: "none", "standardize", "normalize", "demean", "descale" (default: "none").

Examples:

>>> arrests = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/USArrests.csv")
>>> fit_h2o = H2OSingularValueDecompositionEstimator(nv=4,
...                                                  transform="standardize",
...                                                  max_iterations=2000)
>>> fit_h2o.train(x=list(range(4)), training_frame=arrests)
>>> fit_h2o

u_name

Frame key to save left singular vectors

Type: str.

Examples:

>>> arrests = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/USArrests.csv")
>>> fit_h2o = H2OSingularValueDecompositionEstimator(u_name="fit_h2o")
>>> fit_h2o.train(x=list(range(4)), training_frame=arrests)
>>> fit_h2o.u_name
>>> fit_h2o

use_all_factor_levels

Whether first factor level is included in each categorical expansion

Type: bool (default: True).

Examples:

>>> arrests = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/USArrests.csv")
>>> fit_h2o = H2OSingularValueDecompositionEstimator(use_all_factor_levels=False)
>>> fit_h2o.train(x=list(range(4)), training_frame=arrests)
>>> fit_h2o

validation_frame

Id of the validation data frame.

Type: H2OFrame.

Examples:

>>> arrests = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/USArrests.csv")
>>> train, valid = arrests.split_frame(ratios=[.8])
>>> fit_h2o = H2OSingularValueDecompositionEstimator()
>>> fit_h2o.train(x=list(range(4)),
...               training_frame=train,
...               validation_frame=valid)
>>> fit_h2o

H2OWord2vecEstimator

class h2o.estimators.word2vec.H2OWord2vecEstimator(**kwargs)

Bases: h2o.estimators.estimator_base.H2OEstimator

Word2Vec

epochs

Number of training iterations to run

Type: int (default: 5).

Examples:

>>> job_titles = h2o.import_file(("https://s3.amazonaws.com/h2o-public-test-data/smalldata/craigslistJobTitles.csv"), 
...                               col_names = ["category", "jobtitle"], 
...                               col_types = ["string", "string"], 
...                               header = 1)
>>> words = job_titles.tokenize(" ")
>>> w2v_model = H2OWord2vecEstimator(sent_sample_rate = 0.0, epochs = 10)
>>> w2v_model.train(training_frame=words)
>>> synonyms = w2v_model.find_synonyms("teacher", count = 5)
>>> print(synonyms)
>>>
>>> w2v_model2 = H2OWord2vecEstimator(sent_sample_rate = 0.0, epochs = 1)
>>> w2v_model2.train(training_frame=words)
>>> synonyms2 = w2v_model2.find_synonyms("teacher", 3)
>>> print(synonyms2)

export_checkpoints_dir

Automatically export generated models to this directory.

Type: str.

Examples:

>>> import tempfile
>>> from os import listdir
>>> job_titles = h2o.import_file(("https://s3.amazonaws.com/h2o-public-test-data/smalldata/craigslistJobTitles.csv"), 
...                               col_names = ["category", "jobtitle"], 
...                               col_types = ["string", "string"], 
...                               header = 1)
>>> checkpoints_dir = tempfile.mkdtemp()
>>> words = job_titles.tokenize(" ")
>>> w2v_model = H2OWord2vecEstimator(epochs=1,
...                                  max_runtime_secs=10,
...                                  export_checkpoints_dir=checkpoints_dir)
>>> w2v_model.train(training_frame=words)
>>> len(listdir(checkpoints_dir))

static from_external(external=<class 'h2o.frame.H2OFrame'>)

Creates new H2OWord2vecEstimator based on an external model.

Parameters:external – H2OFrame with an external model Returns:H2OWord2vecEstimator instance representing the external model Examples:

>>> words = h2o.create_frame(rows=10, cols=1,
...                          string_fraction=1.0,
...                          missing_fraction=0.0)
>>> embeddings = h2o.create_frame(rows=10, cols=100,
...                               real_fraction=1.0,
...                               missing_fraction=0.0)
>>> word_embeddings = words.cbind(embeddings)
>>> w2v_model = H2OWord2vecEstimator.from_external(external=word_embeddings)

init_learning_rate

Set the starting learning rate

Type: float (default: 0.025).

Examples:

>>> job_titles = h2o.import_file(("https://s3.amazonaws.com/h2o-public-test-data/smalldata/craigslistJobTitles.csv"), 
...                               col_names = ["category", "jobtitle"], 
...                               col_types = ["string", "string"], 
...                               header = 1)
>>> words = job_titles.tokenize(" ")
>>> w2v_model = H2OWord2vecEstimator(epochs=3, init_learning_rate=0.05)
>>> w2v_model.train(training_frame=words)
>>> synonyms = w2v_model.find_synonyms("assistant", 3)
>>> print(synonyms)

max_runtime_secs

Maximum allowed runtime in seconds for model training. Use 0 to disable.

Type: float (default: 0).

Examples:

>>> job_titles = h2o.import_file(("https://s3.amazonaws.com/h2o-public-test-data/smalldata/craigslistJobTitles.csv"), 
...                               col_names = ["category", "jobtitle"], 
...                               col_types = ["string", "string"], 
...                               header = 1)
>>> words = job_titles.tokenize(" ")
>>> w2v_model = H2OWord2vecEstimator(epochs=1, max_runtime_secs=10)
>>> w2v_model.train(training_frame=words)
>>> synonyms = w2v_model.find_synonyms("tutor", 3)
>>> print(synonyms)

min_word_freq

This will discard words that appear less than <int> times

Type: int (default: 5).

Examples:

>>> job_titles = h2o.import_file(("https://s3.amazonaws.com/h2o-public-test-data/smalldata/craigslistJobTitles.csv"), 
...                               col_names = ["category", "jobtitle"], 
...                               col_types = ["string", "string"], 
...                               header = 1)
>>> words = job_titles.tokenize(" ")
>>> w2v_model = H2OWord2vecEstimator(epochs=1, min_word_freq=4)
>>> w2v_model.train(training_frame=words)
>>> synonyms = w2v_model.find_synonyms("teacher", 3)
>>> print(synonyms)

norm_model

Use Hierarchical Softmax

One of: "hsm" (default: "hsm").

Examples:

>>> job_titles = h2o.import_file(("https://s3.amazonaws.com/h2o-public-test-data/smalldata/craigslistJobTitles.csv"), 
...                               col_names = ["category", "jobtitle"], 
...                               col_types = ["string", "string"], 
...                               header = 1)
>>> words = job_titles.tokenize(" ")
>>> w2v_model = H2OWord2vecEstimator(epochs=1, norm_model="hsm")
>>> w2v_model.train(training_frame=words)
>>> synonyms = w2v_model.find_synonyms("teacher", 3)
>>> print(synonyms)

pre_trained

Id of a data frame that contains a pre-trained (external) word2vec model

Type: H2OFrame.

Examples:

>>> words = h2o.create_frame(rows=1000,cols=1,
...                          string_fraction=1.0,
...                          missing_fraction=0.0)
>>> embeddings = h2o.create_frame(rows=1000,cols=100,
...                               real_fraction=1.0,
...                               missing_fraction=0.0)
>>> word_embeddings = words.cbind(embeddings)
>>> w2v_model = H2OWord2vecEstimator(pre_trained=word_embeddings)
>>> w2v_model.train(training_frame=word_embeddings)
>>> model_id = w2v_model.model_id
>>> model = h2o.get_model(model_id)

sent_sample_rate

Set threshold for occurrence of words. Those that appear with higher frequency in the training data will be randomly down-sampled; useful range is (0, 1e-5)

Type: float (default: 0.001).

Examples:

>>> job_titles = h2o.import_file(("https://s3.amazonaws.com/h2o-public-test-data/smalldata/craigslistJobTitles.csv"), 
...                               col_names = ["category", "jobtitle"], 
...                               col_types = ["string", "string"], 
...                               header = 1)
>>> words = job_titles.tokenize(" ")
>>> w2v_model = H2OWord2vecEstimator(epochs=1, sent_sample_rate=0.01)
>>> w2v_model.train(training_frame=words)
>>> synonyms = w2v_model.find_synonyms("teacher", 3)
>>> print(synonyms)

training_frame

Id of the training data frame.

Type: H2OFrame.

Examples:

>>> job_titles = h2o.import_file(("https://s3.amazonaws.com/h2o-public-test-data/smalldata/craigslistJobTitles.csv"), 
...                               col_names = ["category", "jobtitle"], 
...                               col_types = ["string", "string"], 
...                               header = 1)
>>> words = job_titles.tokenize(" ")
>>> w2v_model = H2OWord2vecEstimator()
>>> w2v_model.train(training_frame=words)
>>> synonyms = w2v_model.find_synonyms("tutor", 3)
>>> print(synonyms)

vec_size

Set size of word vectors

Type: int (default: 100).

Examples:

>>> job_titles = h2o.import_file(("https://s3.amazonaws.com/h2o-public-test-data/smalldata/craigslistJobTitles.csv"), 
...                               col_names = ["category", "jobtitle"], 
...                               col_types = ["string", "string"], 
...                               header = 1)
>>> words = job_titles.tokenize(" ")
>>> w2v_model = H2OWord2vecEstimator(epochs=3, vec_size=50)
>>> w2v_model.train(training_frame=words)
>>> synonyms = w2v_model.find_synonyms("tutor", 3)
>>> print(synonyms)

window_size

Set max skip length between words

Type: int (default: 5).

Examples:

>>> job_titles = h2o.import_file(("https://s3.amazonaws.com/h2o-public-test-data/smalldata/craigslistJobTitles.csv"), 
...                               col_names = ["category", "jobtitle"], 
...                               col_types = ["string", "string"], 
...                               header = 1)
>>> words = job_titles.tokenize(" ")
>>> w2v_model = H2OWord2vecEstimator(epochs=3, window_size=2)
>>> w2v_model.train(training_frame=words)
>>> synonyms = w2v_model.find_synonyms("teacher", 3)
>>> print(synonyms)

word_model

Use the Skip-Gram model

One of: "skip_gram" (default: "skip_gram").

Examples:

>>> job_titles = h2o.import_file(("https://s3.amazonaws.com/h2o-public-test-data/smalldata/craigslistJobTitles.csv"), 
...                               col_names = ["category", "jobtitle"], 
...                               col_types = ["string", "string"], 
...                               header = 1)
>>> words = job_titles.tokenize(" ")
>>> w2v_model = H2OWord2vecEstimator(epochs=3, word_model="skip_gram")
>>> w2v_model.train(training_frame=words)
>>> synonyms = w2v_model.find_synonyms("assistant", 3)
>>> print(synonyms)

H2OGridSearch

class h2o.grid.H2OGridSearch(*args, **kwargs)

Bases: h2o.grid.grid_search.H2OGridSearch

Grid Search of a Hyper-Parameter Space for a Model

Parameters:

• model – The type of model to be explored initialized with optional parameters that will be unchanged across explored models.
• hyper_params – A dictionary of string parameters (keys) and a list of values to be explored by grid search (values).
• grid_id (str) – The unique id assigned to the resulting grid object. If none is given, an id will automatically be generated.
• search_criteria –

  The optional dictionary of directives which control the search of the hyperparameter space. The dictionary can include values for: strategy, max_models, max_runtime_secs, stopping_metric, stopping_tolerance, stopping_rounds and seed. The default strategy, “Cartesian”, covers the entire space of hyperparameter combinations. If you want to use cartesian grid search, you can leave the search_criteria argument unspecified. Specify the “RandomDiscrete” strategy to get random search of all the combinations of your hyperparameters with three ways of specifying when to stop the search: max number of models, max time, and metric-based early stopping (e.g., stop if MSE hasn’t improved by 0.0001 over the 5 best models). Examples below:

  >>> criteria = {"strategy": "RandomDiscrete", "max_runtime_secs": 600,
  ...             "max_models": 100, "stopping_metric": "AUTO",
  ...             "stopping_tolerance": 0.00001, "stopping_rounds": 5,
  ...             "seed": 123456}
  >>> criteria = {"strategy": "RandomDiscrete", "max_models": 42,
  ...             "max_runtime_secs": 28800, "seed": 1234}
  >>> criteria = {"strategy": "RandomDiscrete", "stopping_metric": "AUTO",
  ...             "stopping_tolerance": 0.001, "stopping_rounds": 10}
  >>> criteria = {"strategy": "RandomDiscrete", "stopping_rounds": 5,
  ...             "stopping_metric": "misclassification",
  ...             "stopping_tolerance": 0.00001}
  

• parallelism – Level of parallelism during grid model building. 1 = sequential building (default). Use the value of 0 for adaptive parallelism - decided by H2O. Any number > 1 sets the exact number of models built in parallel.

Returns:

a new H2OGridSearch instance

Examples

>>> from h2o.grid.grid_search import H2OGridSearch
>>> from h2o.estimators.glm import H2OGeneralizedLinearEstimator
>>> hyper_parameters = {'alpha': [0.01,0.5], 'lambda': [1e-5,1e-6]}
>>> gs = H2OGridSearch(H2OGeneralizedLinearEstimator(family='binomial'), hyper_parameters)
>>> training_data = h2o.import_file("smalldata/logreg/benign.csv")
>>> gs.train(x=range(3) + range(4,11),y=3, training_frame=training_data)
>>> gs.show()

aic(train=False, valid=False, xval=False)

Get the AIC(s).

If all are False (default), then return the training metric value. If more than one options is set to True, then return a dictionary of metrics where the keys are “train”, “valid”, and “xval”.

Parameters:

• train (bool) – If train is True, then return the AIC value for the training data.
• valid (bool) – If valid is True, then return the AIC value for the validation data.
• xval (bool) – If xval is True, then return the AIC value for the validation data.

Returns:

The AIC.

auc(train=False, valid=False, xval=False)

Get the AUC(s).

If all are False (default), then return the training metric value. If more than one options is set to True, then return a dictionary of metrics where the keys are “train”, “valid”, and “xval”.

Parameters:

• train (bool) – If train is True, then return the AUC value for the training data.
• valid (bool) – If valid is True, then return the AUC value for the validation data.
• xval (bool) – If xval is True, then return the AUC value for the validation data.

Returns:

The AUC.

aucpr(train=False, valid=False, xval=False)

Get the aucPR (Area Under PRECISION RECALL Curve).

If all are False (default), then return the training metric value. If more than one options is set to True, then return a dictionary of metrics where the keys are “train”, “valid”, and “xval”.

Parameters:

• train (bool) – If train is True, then return the aucpr value for the training data.
• valid (bool) – If valid is True, then return the aucpr value for the validation data.
• xval (bool) – If xval is True, then return the aucpr value for the validation data.

Returns:

The AUCPR for the models in this grid.

biases(vector_id=0)

Return the frame for the respective bias vector.

Param:vector_id: an integer, ranging from 0 to number of layers, that specifies the bias vector to return. Returns:an H2OFrame which represents the bias vector identified by vector_id

build_model(algo_params)

(internal)

catoffsets()

Categorical offsets for one-hot encoding

coef()

Return the coefficients that can be applied to the non-standardized data.

Note: standardize = True by default. If set to False, then coef() returns the coefficients that are fit directly.

coef_norm()

Return coefficients fitted on the standardized data (requires standardize = True, which is on by default). These coefficients can be used to evaluate variable importance.

deepfeatures(test_data, layer)

Obtain a hidden layer’s details on a dataset.

Parameters:

• test_data – Data to create a feature space on.
• layer (int) – Index of the hidden layer.

Returns:

A dictionary of hidden layer details for each model.

get_grid(sort_by=None, decreasing=None)

Retrieve an H2OGridSearch instance.

Optionally specify a metric by which to sort models and a sort order. Note that if neither cross-validation nor a validation frame is used in the grid search, then the training metrics will display in the “get grid” output. If a validation frame is passed to the grid, and nfolds = 0, then the validation metrics will display. However, if nfolds > 1, then cross-validation metrics will display even if a validation frame is provided.

Parameters:

• sort_by (str) – A metric by which to sort the models in the grid space. Choices are: "logloss", "residual_deviance", "mse", "auc", "r2", "accuracy", "precision", "recall", "f1", etc.
• decreasing (bool) – Sort the models in decreasing order of metric if true, otherwise sort in increasing order (default).

Returns:

A new H2OGridSearch instance optionally sorted on the specified metric.

get_hyperparams(id, display=True)

Get the hyperparameters of a model explored by grid search.

Parameters:

• id (str) – The model id of the model with hyperparameters of interest.
• display (bool) – Flag to indicate whether to display the hyperparameter names.

Returns:

A list of the hyperparameters for the specified model.

get_hyperparams_dict(id, display=True)

Derived and returned the model parameters used to train the particular grid search model.

Parameters:

• id (str) – The model id of the model with hyperparameters of interest.
• display (bool) – Flag to indicate whether to display the hyperparameter names.

Returns:

A dict of model pararmeters derived from the hyper-parameters used to train this particular model.

get_xval_models(key=None)

Return a Model object.

Parameters:key (str) – If None, return all cross-validated models; otherwise return the model specified by the key. Returns:A model or a list of models.

gini(train=False, valid=False, xval=False)

Get the Gini Coefficient(s).

If all are False (default), then return the training metric value. If more than one options is set to True, then return a dictionary of metrics where the keys are “train”, “valid”, and “xval”.

Parameters:

• train (bool) – If train is True, then return the Gini Coefficient value for the training data.
• valid (bool) – If valid is True, then return the Gini Coefficient value for the validation data.
• xval (bool) – If xval is True, then return the Gini Coefficient value for the cross validation data.

Returns:

The Gini Coefficient for the models in this grid.

grid_id

A key that identifies this grid search object in H2O.

is_cross_validated()

Return True if the model was cross-validated.

join()

Wait until grid finishes computing.

logloss(train=False, valid=False, xval=False)

Get the Log Loss(s).

If all are False (default), then return the training metric value. If more than one options is set to True, then return a dictionary of metrics where the keys are “train”, “valid”, and “xval”.

Parameters:

• train (bool) – If train is True, then return the Log Loss value for the training data.
• valid (bool) – If valid is True, then return the Log Loss value for the validation data.
• xval (bool) – If xval is True, then return the Log Loss value for the cross validation data.

Returns:

The Log Loss for this binomial model.

mean_residual_deviance(train=False, valid=False, xval=False)

Get the Mean Residual Deviances(s).

If all are False (default), then return the training metric value. If more than one options is set to True, then return a dictionary of metrics where the keys are “train”, “valid”, and “xval”.

Parameters:

• train (bool) – If train is True, then return the Mean Residual Deviance value for the training data.
• valid (bool) – If valid is True, then return the Mean Residual Deviance value for the validation data.
• xval (bool) – If xval is True, then return the Mean Residual Deviance value for the cross validation data.

Returns:

The Mean Residual Deviance for this regression model.

model_performance(test_data=None, train=False, valid=False, xval=False)

Generate model metrics for this model on test_data.

Parameters:

• test_data – Data set for which model metrics shall be computed against. All three of train, valid and xval arguments are ignored if test_data is not None.
• train – Report the training metrics for the model.
• valid – Report the validation metrics for the model.
• xval – Report the validation metrics for the model.

Returns:

An object of class H2OModelMetrics.

mse(train=False, valid=False, xval=False)

Get the MSE(s).

If all are False (default), then return the training metric value. If more than one options is set to True, then return a dictionary of metrics where the keys are “train”, “valid”, and “xval”.

Parameters:

• train (bool) – If train is True, then return the MSE value for the training data.
• valid (bool) – If valid is True, then return the MSE value for the validation data.
• xval (bool) – If xval is True, then return the MSE value for the cross validation data.

Returns:

The MSE for this regression model.

normmul()

Normalization/Standardization multipliers for numeric predictors.

normsub()

Normalization/Standardization offsets for numeric predictors.

null_degrees_of_freedom(train=False, valid=False, xval=False)

Retreive the null degress of freedom if this model has the attribute, or None otherwise.

Parameters:

• train (bool) – Get the null dof for the training set. If both train and valid are False, then train is selected by default.
• valid (bool) – Get the null dof for the validation set. If both train and valid are True, then train is selected by default.
• xval (bool) – Get the null dof for the cross-validated models.

Returns:

the null dof, or None if it is not present.

null_deviance(train=False, valid=False, xval=False)

Retreive the null deviance if this model has the attribute, or None otherwise.

Parameters:

• train (bool) – Get the null deviance for the training set. If both train and valid are False, then train is selected by default.
• valid (bool) – Get the null deviance for the validation set. If both train and valid are True, then train is selected by default.
• xval (bool) – Get the null deviance for the cross-validated models.

Returns:

the null deviance, or None if it is not present.

pprint_coef()

Pretty print the coefficents table (includes normalized coefficients).

pr_auc()

H2OGridSearch.pr_auc is deprecated, please use H2OGridSearch.aucpr instead.

predict(test_data)

Predict on a dataset.

Parameters:test_data (H2OFrame) – Data to be predicted on. Returns:H2OFrame filled with predictions.

r2(train=False, valid=False, xval=False)

Return the R^2 for this regression model.

The R^2 value is defined to be 1 - MSE/var, where var is computed as sigma^2.

If all are False (default), then return the training metric value. If more than one options is set to True, then return a dictionary of metrics where the keys are “train”, “valid”, and “xval”.

Parameters:

• train (bool) – If train is True, then return the R^2 value for the training data.
• valid (bool) – If valid is True, then return the R^2 value for the validation data.
• xval (bool) – If xval is True, then return the R^2 value for the cross validation data.

Returns:

The R^2 for this regression model.

residual_degrees_of_freedom(train=False, valid=False, xval=False)

Retreive the residual degress of freedom if this model has the attribute, or None otherwise.

Parameters:

• train (bool) – Get the residual dof for the training set. If both train and valid are False, then train is selected by default.
• valid (bool) – Get the residual dof for the validation set. If both train and valid are True, then train is selected by default.
• xval (bool) – Get the residual dof for the cross-validated models.

Returns:

the residual degrees of freedom, or None if they are not present.

residual_deviance(train=False, valid=False, xval=False)

Retreive the residual deviance if this model has the attribute, or None otherwise.

Parameters:

• train (bool) – Get the residual deviance for the training set. If both train and valid are False, then train is selected by default.
• valid (bool) – Get the residual deviance for the validation set. If both train and valid are True, then train is selected by default.
• xval (bool) – Get the residual deviance for the cross-validated models.

Returns:

the residual deviance, or None if it is not present.

respmul()

Normalization/Standardization multipliers for numeric response.

respsub()

Normalization/Standardization offsets for numeric response.

scoring_history()

Retrieve model scoring history.

Returns:Score history (H2OTwoDimTable)

show()

Print models sorted by metric.

sort_by(metric, increasing=True)

grid.sort_by() is deprecated; use grid.get_grid() instead

Deprecated since 2016-12-12, use grid.get_grid() instead.

sorted_metric_table()

Retrieve summary table of an H2O Grid Search.

Returns:The summary table as an H2OTwoDimTable or a Pandas DataFrame.

start(x, y=None, training_frame=None, offset_column=None, fold_column=None, weights_column=None, validation_frame=None, **params)

Asynchronous model build by specifying the predictor columns, response column, and any additional frame-specific values.

To block for results, call join().

Parameters:

• x – A list of column names or indices indicating the predictor columns.
• y – An index or a column name indicating the response column.
• training_frame – The H2OFrame having the columns indicated by x and y (as well as any additional columns specified by fold, offset, and weights).
• offset_column – The name or index of the column in training_frame that holds the offsets.
• fold_column – The name or index of the column in training_frame that holds the per-row fold assignments.
• weights_column – The name or index of the column in training_frame that holds the per-row weights.
• validation_frame – H2OFrame with validation data to be scored on while training.

summary(header=True)

Print a detailed summary of the explored models.

train(x=None, y=None, training_frame=None, offset_column=None, fold_column=None, weights_column=None, validation_frame=None, **params)

Train the model synchronously (i.e. do not return until the model finishes training).

To train asynchronously call start().

Parameters:

• x – A list of column names or indices indicating the predictor columns.
• y – An index or a column name indicating the response column.
• training_frame – The H2OFrame having the columns indicated by x and y (as well as any additional columns specified by fold, offset, and weights).
• offset_column – The name or index of the column in training_frame that holds the offsets.
• fold_column – The name or index of the column in training_frame that holds the per-row fold assignments.
• weights_column – The name or index of the column in training_frame that holds the per-row weights.
• validation_frame – H2OFrame with validation data to be scored on while training.

varimp(use_pandas=False)

Pretty print the variable importances, or return them in a list/pandas DataFrame.

Parameters:use_pandas (bool) – If True, then the variable importances will be returned as a pandas data frame. Returns:A dictionary of lists or Pandas DataFrame instances.

weights(matrix_id=0)

Return the frame for the respective weight matrix.

Param:matrix_id: an integer, ranging from 0 to number of layers, that specifies the weight matrix to return. Returns:an H2OFrame which represents the weight matrix identified by matrix_id

xval_keys()

Model keys for the cross-validated model.

xvals()

Return the list of cross-validated models.