water.bindings.pojos

Class StackedEnsembleParametersV99

java.lang.Object

water.bindings.pojos.SchemaV3

water.bindings.pojos.ModelParametersSchemaV3

water.bindings.pojos.StackedEnsembleParametersV99

public class StackedEnsembleParametersV99
extends ModelParametersSchemaV3

Field Summary

Fields

Modifier and Type	Field and Description
KeyV3[]	baseModels List of models or grids (or their ids) to ensemble/stack together.
FrameKeyV3	blendingFrame Frame used to compute the predictions that serve as the training frame for the metalearner (triggers blending mode if provided)
boolean	keepLeveloneFrame Keep level one frame used for metalearner training.
EnsembleMetalearnerAlgorithm	metalearnerAlgorithm Type of algorithm to use as the metalearner.
ModelParametersFoldAssignmentScheme	metalearnerFoldAssignment Cross-validation fold assignment scheme for metalearner cross-validation.
ColSpecifierV3	metalearnerFoldColumn Column with cross-validation fold index assignment per observation for cross-validation of the metalearner.
int	metalearnerNfolds Number of folds for K-fold cross-validation of the metalearner algorithm (0 to disable or >= 2).
java.lang.String	metalearnerParams Parameters for metalearner algorithm
long	scoreTrainingSamples Specify the number of training set samples for scoring.
long	seed Seed for random numbers; passed through to the metalearner algorithm.

Fields inherited from class water.bindings.pojos.ModelParametersSchemaV3

aucType, categoricalEncoding, checkpoint, customDistributionFunc, customMetricFunc, distribution, exportCheckpointsDir, foldAssignment, foldColumn, gainsliftBins, huberAlpha, ignoreConstCols, ignoredColumns, keepCrossValidationFoldAssignment, keepCrossValidationModels, keepCrossValidationPredictions, maxCategoricalLevels, maxRuntimeSecs, modelId, nfolds, offsetColumn, parallelizeCrossValidation, quantileAlpha, responseColumn, scoreEachIteration, stoppingMetric, stoppingRounds, stoppingTolerance, trainingFrame, tweediePower, validationFrame, weightsColumn

Constructor Summary

Constructors

Constructor and Description
StackedEnsembleParametersV99() Public constructor

Method Summary

Modifier and Type	Method and Description
java.lang.String	toString() Return the contents of this object as a JSON String.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Field Detail

baseModels

@SerializedName(value="base_models")
public KeyV3[] baseModels

List of models or grids (or their ids) to ensemble/stack together. Grids are expanded to individual models. If not using blending frame, then models must have been cross-validated using nfolds > 1, and folds must be identical across models.

metalearnerAlgorithm

@SerializedName(value="metalearner_algorithm")
public EnsembleMetalearnerAlgorithm metalearnerAlgorithm

Type of algorithm to use as the metalearner. Options include 'AUTO' (GLM with non negative weights; if validation_frame is present, a lambda search is performed), 'deeplearning' (Deep Learning with default parameters), 'drf' (Random Forest with default parameters), 'gbm' (GBM with default parameters), 'glm' (GLM with default parameters), 'naivebayes' (NaiveBayes with default parameters), or 'xgboost' (if available, XGBoost with default parameters).

metalearnerNfolds

@SerializedName(value="metalearner_nfolds")
public int metalearnerNfolds

Number of folds for K-fold cross-validation of the metalearner algorithm (0 to disable or >= 2).

metalearnerFoldAssignment

@SerializedName(value="metalearner_fold_assignment")
public ModelParametersFoldAssignmentScheme metalearnerFoldAssignment

Cross-validation fold assignment scheme for metalearner cross-validation. Defaults to AUTO (which is currently set to Random). The 'Stratified' option will stratify the folds based on the response variable, for classification problems.

metalearnerFoldColumn

@SerializedName(value="metalearner_fold_column")
public ColSpecifierV3 metalearnerFoldColumn

Column with cross-validation fold index assignment per observation for cross-validation of the metalearner.

keepLeveloneFrame

@SerializedName(value="keep_levelone_frame")
public boolean keepLeveloneFrame

Keep level one frame used for metalearner training.

metalearnerParams

@SerializedName(value="metalearner_params")
public java.lang.String metalearnerParams

Parameters for metalearner algorithm

blendingFrame

@SerializedName(value="blending_frame")
public FrameKeyV3 blendingFrame

Frame used to compute the predictions that serve as the training frame for the metalearner (triggers blending mode if provided)

seed

public long seed

Seed for random numbers; passed through to the metalearner algorithm. Defaults to -1 (time-based random number)

scoreTrainingSamples

@SerializedName(value="score_training_samples")
public long scoreTrainingSamples

Specify the number of training set samples for scoring. The value must be >= 0. To use all training samples, enter 0.

Constructor Detail

StackedEnsembleParametersV99

public StackedEnsembleParametersV99()

Public constructor

Method Detail

toString

public java.lang.String toString()

Return the contents of this object as a JSON String.

Overrides:

toString in class ModelParametersSchemaV3