.. _parameters_H2OExtendedIsolationForest:

Parameters of H2OExtendedIsolationForest
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Affected Class
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- ``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:* ``None``
  
  *Also 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:* ``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.*

disableTrainingMetrics
  Disable calculating training metrics (expensive on large datasets).

  *Scala default value:* ``true`` *; Python default value:* ``True``
  
  *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:* ``0``
  
  *Also 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:* ``True``
  
  *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``
  

modelId
  Destination id for this model; auto-generated if not specified.

  *Scala default value:* ``null`` *; Python default value:* ``None``
  

ntrees
  Number of Extended Isolation Forest trees.

  *Default value:* ``100``
  
  *Also 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:* ``256``
  
  *Also available on the trained model.*

scoreEachIteration
  Whether to score during each iteration of model training.

  *Scala default value:* ``false`` *; Python default value:* ``False``
  
  *Also available on the trained model.*

scoreTreeInterval
  Score the model after every so many trees. Disabled if set to 0.

  *Default value:* ``0``
  
  *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``
  

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.*