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.

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.

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

namedMojoOutputColumns

Mojo Output is not stored in the array but in the properly named columns

Scala default value: true ; Python default value: True

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.

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.

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.

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.

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.