.. _parameters_H2OIsolationForest:
Parameters of H2OIsolationForest
--------------------------------
Affected Class
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- ``ai.h2o.sparkling.ml.algos.H2OIsolationForest``
Parameters
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- *Each parameter has also a corresponding getter and setter method.*
*(E.g.:* ``label`` *->* ``getLabel()`` *,* ``setLabel(...)`` *)*
calibrationDataFrame
Calibration frame for Platt Scaling. To enable usage of the data frame, set the parameter calibrateModel to True.
*Scala default value:* ``null`` *; Python default value:* ``None``
ignoredCols
Names of columns to ignore for training.
*Scala default value:* ``null`` *; Python default value:* ``None``
*Also available on the trained model.*
buildTreeOneNode
Run on one node only; no network overhead but fewer cpus used. Suitable for small datasets.
*Scala default value:* ``false`` *; Python default value:* ``False``
*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.*
colSampleRateChangePerLevel
Relative change of the column sampling rate for every level (must be > 0.0 and <= 2.0).
*Default value:* ``1.0``
*Also available on the trained model.*
colSampleRatePerTree
Column sample rate per tree (from 0.0 to 1.0).
*Default value:* ``1.0``
*Also available on the trained model.*
columnsToCategorical
List of columns to convert to categorical before modelling
*Scala default value:* ``Array()`` *; Python default value:* ``[]``
contamination
Contamination ratio - the proportion of anomalies in the input dataset. If undefined (-1) the predict function will not mark observations as anomalies and only anomaly score will be returned. Defaults to -1 (undefined).
*Default value:* ``-1.0``
*Also available on the trained model.*
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.*
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``
maxDepth
Maximum tree depth (0 for unlimited).
*Default value:* ``8``
*Also available on the trained model.*
maxRuntimeSecs
Maximum allowed runtime in seconds for model training. Use 0 to disable.
*Default value:* ``0.0``
*Also available on the trained model.*
minRows
Fewest allowed (weighted) observations in a leaf.
*Default value:* ``1.0``
*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``
mtries
Number of variables randomly sampled as candidates at each split. If set to -1, defaults (number of predictors)/3.
*Default value:* ``-1``
*Also available on the trained model.*
ntrees
Number of trees.
*Default value:* ``50``
*Also available on the trained model.*
predictionCol
Prediction column name
*Default value:* ``"prediction"``
*Also available on the trained model.*
sampleRate
Rate of randomly sampled observations used to train each Isolation Forest tree. Needs to be in range from 0.0 to 1.0. If set to -1, sample_rate is disabled and sample_size will be used instead.
*Default value:* ``-1.0``
*Also available on the trained model.*
sampleSize
Number of randomly sampled observations used to train each Isolation Forest tree. Only one of parameters sample_size and sample_rate should be defined. If sample_rate is defined, sample_size will be ignored.
*Scala default value:* ``256L`` *; Python 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``
stoppingMetric
Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client. Possible values are ``"AUTO"``, ``"deviance"``, ``"logloss"``, ``"MSE"``, ``"RMSE"``, ``"MAE"``, ``"RMSLE"``, ``"AUC"``, ``"AUCPR"``, ``"lift_top_group"``, ``"misclassification"``, ``"mean_per_class_error"``, ``"anomaly_score"``, ``"AUUC"``, ``"ATE"``, ``"ATT"``, ``"ATC"``, ``"qini"``, ``"custom"``, ``"custom_increasing"``.
*Default value:* ``"AUTO"``
*Also available on the trained model.*
stoppingRounds
Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable).
*Default value:* ``0``
*Also available on the trained model.*
stoppingTolerance
Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much).
*Default value:* ``0.01``
*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``
validationLabelCol
(experimental) Name of the label column in the validation data frame. The label column should be a string column with two distinct values indicating the anomaly. The negative value must be alphabetically smaller than the positive value. (E.g. '0'/'1', 'False'/'True'
*Default value:* ``"label"``
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.*