.. _parameters_H2OStackedEnsemble: Parameters of H2OStackedEnsemble -------------------------------- Affected Class ############## - ``ai.h2o.sparkling.ml.algos.H2OStackedEnsemble`` Parameters ########## - *Each parameter has also a corresponding getter and setter method.* *(E.g.:* ``label`` *->* ``getLabel()`` *,* ``setLabel(...)`` *)* baseAlgorithms An array of base algorithms *Scala default value:* ``null`` *; Python default value:* ``None`` blendingDataFrame This parameter is used for computing the predictions that serve as the training frame for the meta-learner. If provided, this triggers blending mode on the stacked ensemble training stage. Blending mode is faster than cross-validating the base learners (though these ensembles may not perform as well as the Super Learner ensemble). The parameter is not serializable! *Scala default value:* ``null`` *; Python default value:* ``None`` aucType Set default multinomial AUC type. Possible values are ``"AUTO"``, ``"NONE"``, ``"MACRO_OVR"``, ``"WEIGHTED_OVR"``, ``"MACRO_OVO"``, ``"WEIGHTED_OVO"``. *Default value:* ``"AUTO"`` *Also available on the trained model.* categoricalEncoding Encoding scheme for categorical features. Possible values are ``"AUTO"``, ``"OneHotInternal"``, ``"OneHotExplicit"``, ``"Enum"``, ``"Binary"``, ``"Eigen"``, ``"LabelEncoder"``, ``"SortByResponse"``, ``"EnumLimited"``. *Default value:* ``"AUTO"`` *Also available on the trained model.* checkpoint Model checkpoint to resume training with. *Scala default value:* ``null`` *; Python default value:* ``None`` *Also available on the trained model.* columnsToCategorical List of columns to convert to categorical before modelling *Scala default value:* ``Array()`` *; Python default value:* ``[]`` convertInvalidNumbersToNa If set to 'true', the model converts invalid numbers to NA during making predictions. *Scala default value:* ``false`` *; Python default value:* ``False`` *Also available on the trained model.* convertUnknownCategoricalLevelsToNa If set to 'true', the model converts unknown categorical levels to NA during making predictions. *Scala default value:* ``false`` *; Python default value:* ``False`` *Also available on the trained model.* customDistributionFunc Reference to custom distribution, format: `language:keyName=funcName`. *Scala default value:* ``null`` *; Python default value:* ``None`` *Also available on the trained model.* customMetricFunc Reference to custom evaluation function, format: `language:keyName=funcName`. *Scala default value:* ``null`` *; Python default value:* ``None`` *Also available on the trained model.* dataFrameSerializer A full name of a serializer used for serialization and deserialization of Spark DataFrames to a JSON value within NullableDataFrameParam. *Default value:* ``"ai.h2o.sparkling.utils.JSONDataFrameSerializer"`` *Also available on the trained model.* detailedPredictionCol Column containing additional prediction details, its content depends on the model type. *Default value:* ``"detailed_prediction"`` *Also available on the trained model.* distribution Distribution function. Possible values are ``"AUTO"``, ``"bernoulli"``, ``"quasibinomial"``, ``"modified_huber"``, ``"multinomial"``, ``"ordinal"``, ``"gaussian"``, ``"poisson"``, ``"gamma"``, ``"tweedie"``, ``"huber"``, ``"laplace"``, ``"quantile"``, ``"fractionalbinomial"``, ``"negativebinomial"``, ``"custom"``. *Default value:* ``"AUTO"`` *Also available on the trained model.* exportCheckpointsDir Automatically export generated models to this directory. *Scala default value:* ``null`` *; Python default value:* ``None`` *Also available on the trained model.* featuresCols Name of feature columns *Scala default value:* ``Array()`` *; Python default value:* ``[]`` *Also available on the trained model.* foldAssignment 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. Possible values are ``"AUTO"``, ``"Random"``, ``"Modulo"``, ``"Stratified"``. *Default value:* ``"AUTO"`` *Also available on the trained model.* foldCol Column with cross-validation fold index assignment per observation. *Scala default value:* ``null`` *; Python default value:* ``None`` *Also available on the trained model.* gainsliftBins Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning. *Default value:* ``-1`` *Also available on the trained model.* huberAlpha Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1). *Default value:* ``0.9`` *Also available on the trained model.* ignoreConstCols Ignore constant columns. *Scala default value:* ``true`` *; Python default value:* ``True`` *Also available on the trained model.* ignoredCols Names of columns to ignore for training. *Scala default value:* ``null`` *; Python default value:* ``None`` *Also available on the trained model.* keepBinaryModels If set to true, all binary models created during execution of the ``fit`` method will be kept in DKV of H2O-3 cluster. *Scala default value:* ``false`` *; Python default value:* ``False`` keepCrossValidationFoldAssignment Whether to keep the cross-validation fold assignment. *Scala default value:* ``false`` *; Python default value:* ``False`` *Also available on the trained model.* keepCrossValidationModels Whether to keep the cross-validation models. *Scala default value:* ``true`` *; Python default value:* ``True`` *Also available on the trained model.* keepCrossValidationPredictions Whether to keep the predictions of the cross-validation models. *Scala default value:* ``false`` *; Python default value:* ``False`` *Also available on the trained model.* keepLeveloneFrame Keep level one frame used for metalearner training. *Scala default value:* ``false`` *; Python default value:* ``False`` *Also available on the trained model.* labelCol Response variable column. *Scala default value:* ``null`` *; Python default value:* ``None`` *Also available on the trained model.* maxCategoricalLevels For every categorical feature, only use this many most frequent categorical levels for model training. Only used for categorical_encoding == EnumLimited. *Default value:* ``10`` *Also available on the trained model.* maxRuntimeSecs Maximum allowed runtime in seconds for model training. Use 0 to disable. *Default value:* ``0.0`` *Also available on the trained model.* metalearnerAlgorithm 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). Possible values are ``"AUTO"``, ``"deeplearning"``, ``"drf"``, ``"gbm"``, ``"glm"``, ``"naivebayes"``, ``"xgboost"``. *Default value:* ``"AUTO"`` *Also available on the trained model.* metalearnerFoldAssignment 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. Possible values are ``"AUTO"``, ``"Random"``, ``"Modulo"``, ``"Stratified"``. *Default value:* ``"AUTO"`` *Also available on the trained model.* metalearnerFoldCol Column with cross-validation fold index assignment per observation for cross-validation of the metalearner. *Scala default value:* ``null`` *; Python default value:* ``None`` *Also available on the trained model.* metalearnerNfolds Number of folds for K-fold cross-validation of the metalearner algorithm (0 to disable or >= 2). *Default value:* ``0`` *Also available on the trained model.* metalearnerParams Parameters for metalearner algorithm. *Default value:* ``""`` *Also available on the trained model.* metalearnerTransform Transformation used for the level one frame. Possible values are ``"NONE"``, ``"Logit"``. *Default value:* ``"NONE"`` *Also available on the trained model.* modelId Destination id for this model; auto-generated if not specified. *Scala default value:* ``null`` *; Python default value:* ``None`` nfolds Number of folds for K-fold cross-validation (0 to disable or >= 2). *Default value:* ``0`` *Also available on the trained model.* offsetCol Offset column. This will be added to the combination of columns before applying the link function. *Scala default value:* ``null`` *; Python default value:* ``None`` *Also available on the trained model.* parallelizeCrossValidation Allow parallel training of cross-validation models. *Scala default value:* ``true`` *; Python default value:* ``True`` predictionCol Prediction column name *Default value:* ``"prediction"`` *Also available on the trained model.* quantileAlpha Desired quantile for Quantile regression, must be between 0 and 1. *Default value:* ``0.5`` *Also available on the trained model.* scoreEachIteration Whether to score during each iteration of model training. *Scala default value:* ``false`` *; Python default value:* ``False`` *Also available on the trained model.* scoreTrainingSamples Specify the number of training set samples for scoring. The value must be >= 0. To use all training samples, enter 0. *Scala default value:* ``10000L`` *; Python default value:* ``10000`` *Also available on the trained model.* seed Seed for random numbers; passed through to the metalearner algorithm. Defaults to -1 (time-based random number). *Scala default value:* ``-1L`` *; Python default value:* ``-1`` *Also available on the trained model.* splitRatio Accepts values in range [0, 1.0] which determine how large part of dataset is used for training and for validation. For example, 0.8 -> 80% training 20% validation. This parameter is ignored when validationDataFrame is set. *Default value:* ``1.0`` stoppingMetric Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client. Possible values are ``"AUTO"``, ``"deviance"``, ``"logloss"``, ``"MSE"``, ``"RMSE"``, ``"MAE"``, ``"RMSLE"``, ``"AUC"``, ``"AUCPR"``, ``"lift_top_group"``, ``"misclassification"``, ``"mean_per_class_error"``, ``"anomaly_score"``, ``"AUUC"``, ``"ATE"``, ``"ATT"``, ``"ATC"``, ``"qini"``, ``"custom"``, ``"custom_increasing"``. *Default value:* ``"AUTO"`` *Also available on the trained model.* stoppingRounds 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). *Default value:* ``0`` *Also available on the trained model.* stoppingTolerance Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much). *Default value:* ``0.001`` *Also available on the trained model.* tweediePower Tweedie power for Tweedie regression, must be between 1 and 2. *Default value:* ``1.5`` *Also available on the trained model.* validationDataFrame A data frame dedicated for a validation of the trained model. If the parameters is not set,a validation frame created via the 'splitRatio' parameter. The parameter is not serializable! *Scala default value:* ``null`` *; Python default value:* ``None`` weightCol 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. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0. *Scala default value:* ``null`` *; Python default value:* ``None`` *Also available on the trained model.* withContributions Enables or disables generating a sub-column of detailedPredictionCol containing Shapley values of original features. *Scala default value:* ``false`` *; Python default value:* ``False`` *Also available on the trained model.* withLeafNodeAssignments Enables or disables computation of leaf node assignments. *Scala default value:* ``false`` *; Python default value:* ``False`` *Also available on the trained model.* withStageResults Enables or disables computation of stage results. *Scala default value:* ``false`` *; Python default value:* ``False`` *Also available on the trained model.*