.. _parameters_H2OIsolationForest: Parameters of H2OIsolationForest -------------------------------- Affected Class ############## - ``ai.h2o.sparkling.ml.algos.H2OIsolationForest`` Parameters ########## - *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`` 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.* ignoredCols Names of columns to ignore for training. *Scala default value:* ``null`` *; Python default value:* ``None`` *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.* 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.* 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 anonomaly_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"``, ``"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. *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.*