Modeling In H2O¶

H2OEstimator¶

class h2o.estimators.estimator_base.H2OEstimator[source]¶

Bases: h2o.model.model_base.ModelBase

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.

Attributes

full_parameters
model_id
params
type
xvals

Methods

aic
auc
biases
build_model
catoffsets
coef
coef_norm
deepfeatures
download_pojo
fit
get_params
get_xval_models
giniCoef
is_cross_validated
join
logloss
mean_residual_deviance
mixin
model_performance
mse
next
normmul
normsub
null_degrees_of_freedom
null_deviance
pprint_coef
predict
r2
residual_degrees_of_freedom
residual_deviance
respmul
respsub
score_history
scoring_history
set_params
show
start
summary
train
varimp
weights
xval_keys

build_model(algo_params)[source]¶

fit(X, y=None, **params)[source]¶

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, optional An H2OFrame consisting of the response variable. params : optional Extra arguments.
Returns:	The current instance of H2OEstimator for method chaining.

get_params(deep=True)[source]¶

Useful method for obtaining parameters for this estimator. Used primarily for sklearn Pipelines and sklearn grid search.

Parameters:	deep : bool, optional If True, return parameters of all sub-objects that are estimators.
Returns:	A dict of parameters

join()[source]¶

static mixin(obj, cls)[source]¶

set_params(**parms)[source]¶

Used by sklearn for updating parameters during grid search.

Parameters:	parms : dict A dictionary of parameters that will be set on this model.
Returns:	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)[source]¶

Asynchronous model build by specifying the predictor columns, response column, and any additional frame-specific values.

To block for results, call join.

Parameters:

x : list A list of column names or indices indicating the predictor columns. y : str 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 : str, optional The name or index of the column in training_frame that holds the offsets. fold_column : str, optional The name or index of the column in training_frame that holds the per-row fold assignments. weights_column : str, optional The name or index of the column in training_frame that holds the per-row weights. validation_frame : H2OFrame, optional H2OFrame with validation data to be scored on while training.

train(x, y=None, training_frame=None, offset_column=None, fold_column=None, weights_column=None, validation_frame=None, max_runtime_secs=None, **params)[source]¶

Train the H2O model by specifying the predictor columns, response column, and any additional frame-specific values.

Parameters:

x : list A list of column names or indices indicating the predictor columns. y : str 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 : str, optional The name or index of the column in training_frame that holds the offsets. fold_column : str, optional The name or index of the column in training_frame that holds the per-row fold assignments. weights_column : str, optional The name or index of the column in training_frame that holds the per-row weights. validation_frame : H2OFrame, optional 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.