.. _parameters_H2ORuleFit: Parameters of H2ORuleFit ------------------------ Affected Classes ################ - ``ai.h2o.sparkling.ml.algos.H2ORuleFit`` - ``ai.h2o.sparkling.ml.algos.classification.H2ORuleFitClassifier`` - ``ai.h2o.sparkling.ml.algos.regression.H2ORuleFitRegressor`` Parameters ########## - *Each parameter has also a corresponding getter and setter method.* *(E.g.:* ``label`` *->* ``getLabel()`` *,* ``setLabel(...)`` *)* ignoredCols Names of columns to ignore for training. *Scala default value:* ``null`` *; Python default value:* ``None`` *Also available on the trained model.* algorithm The algorithm to use to generate rules. Possible values are ``"DRF"``, ``"GBM"``, ``"AUTO"``. *Default value:* ``"AUTO"`` *Also available on the trained model.* 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.* 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.* 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.* featuresCols Name of feature columns *Scala default value:* ``Array()`` *; Python default value:* ``[]`` *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`` labelCol Response variable column. *Default value:* ``"label"`` *Also available on the trained model.* lambdaValue Lambda for LASSO regressor. *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.* maxNumRules The maximum number of rules to return. defaults to -1 which means the number of rules is selectedby diminishing returns in model deviance. *Default value:* ``-1`` *Also available on the trained model.* maxRuleLength Maximum length of rules. Defaults to 3. *Default value:* ``3`` *Also available on the trained model.* minRuleLength Minimum length of rules. Defaults to 3. *Default value:* ``3`` *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`` modelType Specifies type of base learners in the ensemble. Possible values are ``"RULES"``, ``"RULES_AND_LINEAR"``, ``"LINEAR"``. *Default value:* ``"RULES_AND_LINEAR"`` *Also available on the trained model.* predictionCol Prediction column name *Default value:* ``"prediction"`` *Also available on the trained model.* removeDuplicates Whether to remove rules which are identical to an earlier rule. Defaults to true. *Scala default value:* ``true`` *; Python default value:* ``True`` *Also available on the trained model.* ruleGenerationNtrees Specifies the number of trees to build in the tree model. Defaults to 50. *Default value:* ``50`` *Also available on the trained model.* seed Seed for pseudo random number generator (if applicable). *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`` 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.*