Parameters of H2OExtendedIsolationForest¶
Affected Class¶
ai.h2o.sparkling.ml.algos.H2OExtendedIsolationForest
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.
- 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.
- 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.
- extensionLevel
Maximum is N - 1 (N = numCols). Minimum is 0. Extended Isolation Forest with extension_Level = 0 behaves like Isolation Forest.
Default value:
0Also available on the trained model.
- featuresCols
Name of feature columns
Scala default value:
Array(); Python default value:[]Also available on the trained model.
- ignoreConstCols
Ignore constant columns.
Scala default value:
true; Python default value:TrueAlso 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- 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.
- ntrees
Number of Extended Isolation Forest trees.
Default value:
100Also available on the trained model.
- predictionCol
Prediction column name
Default value:
"prediction"Also available on the trained model.
- sampleSize
Number of randomly sampled observations used to train each Extended Isolation Forest tree.
Default value:
256Also available on the trained model.
- seed
Seed for pseudo random number generator (if applicable).
Scala default value:
-1L; Python default value:-1Also 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- withContributions
Enables or disables generating a sub-column of detailedPredictionCol containing Shapley values of original features.
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.