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:NoneAlso 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:FalseAlso 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:FalseAlso 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:NoneAlso 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:
0.0Also 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:NoneAlso 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:NoneAlso available on the trained model.
- keepBinaryModels
If set to true, all binary models created during execution of the
fitmethod 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:
9.0Also available on the trained model.
- maxIterations
Maximum number of iterations.
Default value:
20Also 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:TrueAlso 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:NoneAlso 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:FalseAlso 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:NoneAlso available on the trained model.
- stopCol
Stop Time Column.
Scala default value:
null; Python default value:NoneAlso available on the trained model.
- stratifyBy
List of columns to use for stratification.
Scala default value:
null; Python default value:NoneAlso 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:FalseAlso 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:NoneAlso 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:FalseAlso available on the trained model.
- withLeafNodeAssignments
Enables or disables computation of leaf node assignments.
Scala default value:
false; Python default value:FalseAlso available on the trained model.
- withStageResults
Enables or disables computation of stage results.
Scala default value:
false; Python default value:FalseAlso available on the trained model.