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 the following algorithms: three pre-specified XGBoost GBM (Gradient Boosting Machine) models, a fixed grid of GLMs, a default Random Forest (DRF), five pre-specified H2O GBMs, a near-default Deep Neural Net, an Extremely Randomized Forest (XRT), a random grid of XGBoost GBMs, a random grid of H2O GBMs, and a random grid of Deep Neural Nets. In some cases, there will not be enough time to complete all the algorithms, so some may be missing from the leaderboard. AutoML trains several Stacked Ensemble models during the run. Two kinds of Stacked Ensemble models are trained one of all available models, and one of only the best models of each kind. Note that Stacked Ensemble models are trained only if there isn't another stacked ensemble with the same base models.

h2o.automl(
  x,
  y,
  training_frame,
  validation_frame = NULL,
  leaderboard_frame = NULL,
  blending_frame = NULL,
  nfolds = 5,
  fold_column = NULL,
  weights_column = NULL,
  balance_classes = FALSE,
  class_sampling_factors = NULL,
  max_after_balance_size = 5,
  max_runtime_secs = NULL,
  max_runtime_secs_per_model = NULL,
  max_models = NULL,
  stopping_metric = c("AUTO", "deviance", "logloss", "MSE", "RMSE", "MAE", "RMSLE",
    "AUC", "AUCPR", "lift_top_group", "misclassification", "mean_per_class_error"),
  stopping_tolerance = NULL,
  stopping_rounds = 3,
  seed = NULL,
  project_name = NULL,
  exclude_algos = NULL,
  include_algos = NULL,
  modeling_plan = NULL,
  preprocessing = NULL,
  exploitation_ratio = -1,
  monotone_constraints = NULL,
  keep_cross_validation_predictions = FALSE,
  keep_cross_validation_models = FALSE,
  keep_cross_validation_fold_assignment = FALSE,
  sort_metric = c("AUTO", "deviance", "logloss", "MSE", "RMSE", "MAE", "RMSLE", "AUC",
    "AUCPR", "mean_per_class_error"),
  export_checkpoints_dir = NULL,
  verbosity = "warn",
  ...
)

Arguments

x	A vector containing the names or indices of the predictor variables to use in building the model. If x is missing, then all columns except y are used.
y	The name or index of the response variable in the model. For classification, the y column must be a factor, otherwise regression will be performed. Indexes are 1-based in R.
training_frame	Training frame (H2OFrame or ID).
validation_frame	Validation frame (H2OFrame or ID); Optional. 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	Leaderboard frame (H2OFrame or ID); Optional. If provided, the Leaderboard will be scored using this data frame intead of using cross-validation metrics, which is the default.
blending_frame	Blending frame (H2OFrame or ID) used to train the the metalearning algorithm in Stacked Ensembles (instead of relying on cross-validated predicted values); Optional. When provided, it also is recommended to disable cross validation by setting `nfolds=0` and to provide a leaderboard frame for scoring purposes.
nfolds	Number of folds for k-fold cross-validation. Defaults to 5. Use 0 to disable cross-validation; this will also disable Stacked Ensemble (thus decreasing the overall model performance).
fold_column	Column with cross-validation fold index assignment per observation; used to override the default, randomized, 5-fold cross-validation scheme for individual models in the AutoML run.
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.
balance_classes	Logical. Balance training data class counts via over/under-sampling (for imbalanced data). Defaults to 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.
max_after_balance_size	Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires balance_classes. Defaults to 5.0.
max_runtime_secs	This argument specifies the maximum time that the AutoML process will run for. If neither `max_runtime_secs` nor `max_models` are specified by the user, then `max_runtime_secs` defaults to 3600 seconds (1 hour).
max_runtime_secs_per_model	Maximum runtime in seconds dedicated to each individual model training process. Use 0 to disable. Defaults to 0.
max_models	Maximum number of models to build in the AutoML process (does not include Stacked Ensembles). Defaults to NULL (no strict limit).
stopping_metric	Metric to use for early stopping ("AUTO" is logloss for classification, deviance for regression). Must be one of "AUTO", "deviance", "logloss", "MSE", "RMSE", "MAE", "RMSLE", "AUC", "AUCPR", "lift_top_group", "misclassification", "mean_per_class_error". Defaults to "AUTO".
stopping_tolerance	Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much). This value defaults to 0.001 if the dataset is at least 1 million rows; otherwise it defaults to a bigger value determined by the size of the dataset and the non-NA-rate. In that case, the value is computed as 1/sqrt(nrows * non-NA-rate).
stopping_rounds	Integer. 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. Defaults to 3 and must be an non-zero integer. Use 0 to disable early stopping.
seed	Integer. Set a seed for reproducibility. AutoML can only guarantee reproducibility if max_models or early stopping is used because max_runtime_secs is resource limited, meaning that if the resources are not the same between runs, AutoML may be able to train more models on one run vs another.
project_name	Character string to identify an AutoML project. Defaults to NULL, which means a project name will be auto-generated.
exclude_algos	Vector of character strings naming the algorithms to skip during the model-building phase. An example use is exclude_algos = c("GLM", "DeepLearning", "DRF"), and the full list of options is: "DRF" (Random Forest and Extremely-Randomized Trees), "GLM", "XGBoost", "GBM", "DeepLearning" and "StackedEnsemble". Defaults to NULL, which means that all appropriate H2O algorithms will be used, if the search stopping criteria allow. Optional.
include_algos	Vector of character strings naming the algorithms to restrict to during the model-building phase. This can't be used in combination with exclude_algos param. Defaults to NULL, which means that all appropriate H2O algorithms will be used, if the search stopping criteria allow. Optional.
modeling_plan	List. The list of modeling steps to be used by the AutoML engine (they may not all get executed, depending on other constraints). Optional (Expert usage only).
preprocessing	List. The list of preprocessing steps to run. Only 'target_encoding' is currently supported.
exploitation_ratio	The budget ratio (between 0 and 1) dedicated to the exploitation (vs exploration) phase. By default, this is set to AUTO (exploitation_ratio=-1) as this is still experimental; to activate it, it is recommended to try a ratio around 0.1. Note that the current exploitation phase only tries to fine-tune the best XGBoost and the best GBM found during exploration.
monotone_constraints	List. A mapping representing monotonic constraints. Use +1 to enforce an increasing constraint and -1 to specify a decreasing constraint.
keep_cross_validation_predictions	Logical. Whether to keep the predictions of the cross-validation predictions. This needs to be set to TRUE if running the same AutoML object for repeated runs because CV predictions are required to build additional Stacked Ensemble models in AutoML. This option defaults to FALSE.
keep_cross_validation_models	Logical. Whether to keep the cross-validated models. Keeping cross-validation models may consume significantly more memory in the H2O cluster. This option defaults to FALSE.
keep_cross_validation_fold_assignment	Logical. Whether to keep fold assignments in the models. Deleting them will save memory in the H2O cluster. Defaults to FALSE.
sort_metric	Metric to sort the leaderboard by. For binomial classification choose between "AUC", "AUCPR", "logloss", "mean_per_class_error", "RMSE", "MSE". For regression choose between "mean_residual_deviance", "RMSE", "MSE", "MAE", and "RMSLE". For multinomial classification choose between "mean_per_class_error", "logloss", "RMSE", "MSE". Default is "AUTO". If set to "AUTO", then "AUC" will be used for binomial classification, "mean_per_class_error" for multinomial classification, and "mean_residual_deviance" for regression.
export_checkpoints_dir	(Optional) Path to a directory where every model will be stored in binary form.
verbosity	Verbosity of the backend messages printed during training; Optional. Must be one of NULL (live log disabled), "debug", "info", "warn". Defaults to "warn".
...	Additional (experimental) arguments to be passed through; Optional.

Value

An H2OAutoML object.

Details

AutoML finds the best model, given a training frame and response, and returns an H2OAutoML object, which contains a leaderboard of all the models that were trained in the process, ranked by a default model performance metric.

Examples

# NOT RUN {
library(h2o)
h2o.init()
prostate_path <- system.file("extdata", "prostate.csv", package = "h2o")
prostate <- h2o.importFile(path = prostate_path, header = TRUE)
y <- "CAPSULE"
prostate[,y] <- as.factor(prostate[,y])  #convert to factor for classification
aml <- h2o.automl(y = y, training_frame = prostate, max_runtime_secs = 30)
lb <- h2o.get_leaderboard(aml)
head(lb)
# }