.. _parameters_H2OCoxPH: Parameters of H2OCoxPH ---------------------- Affected Class ############## - ``ai.h2o.sparkling.ml.algos.H2OCoxPH`` 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.* interactionPairs A list of pairwise (first order) column interactions. *Scala default value:* ``null`` *; Python default value:* ``None`` 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.* 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.* init Coefficient starting value. *Default value:* ``0.0`` *Also available on the trained model.* interactions A list of predictor column indices to interact. All pairwise combinations will be computed for the list. *Scala default value:* ``null`` *; Python default value:* ``None`` *Also available on the trained model.* interactionsOnly A list of columns that should only be used to create interactions but should not itself participate in model 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`` labelCol Response variable column. *Default value:* ``"label"`` *Also available on the trained model.* lreMin Minimum log-relative error. *Default value:* ``9.0`` *Also available on the trained model.* maxIterations Maximum number of iterations. *Default value:* ``20`` *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`` 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.* predictionCol Prediction column name *Default value:* ``"prediction"`` *Also available on the trained model.* singleNodeMode Run on a single node to reduce the effect of network overhead (for smaller datasets). *Scala default value:* ``false`` *; Python default value:* ``False`` *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`` startCol Start Time Column. *Scala default value:* ``null`` *; Python default value:* ``None`` *Also available on the trained model.* stopCol Stop Time Column. *Scala default value:* ``null`` *; Python default value:* ``None`` *Also available on the trained model.* stratifyBy List of columns to use for stratification. *Scala default value:* ``null`` *; Python default value:* ``None`` *Also available on the trained model.* ties Method for Handling Ties. Possible values are ``"efron"``, ``"breslow"``. *Default value:* ``"efron"`` *Also available on the trained model.* useAllFactorLevels (Internal. For development only!) Indicates whether to use all factor levels. *Scala default value:* ``false`` *; Python default value:* ``False`` *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.*