Uses of Class
org.apache.ignite.ml.composition.ModelsComposition

Packages that use ModelsComposition

Package	Description
org.apache.ignite.ml.composition	Contains classes for ensemble of models implementation.
org.apache.ignite.ml.composition.boosting	Contains Gradient Boosting regression and classification abstract classes allowing regressor type selecting in child classes.
org.apache.ignite.ml.composition.boosting.convergence	Package contains implementation of convergency checking algorithms for gradient boosting.
org.apache.ignite.ml.composition.boosting.convergence.mean	Contains implementation of convergence checking computer by mean of absolute value of errors in dataset.
org.apache.ignite.ml.composition.boosting.convergence.median	Contains implementation of convergence checking computer by median of medians of errors in dataset.
org.apache.ignite.ml.composition.boosting.convergence.simple	Contains implementation of Stub for convergence checking.
org.apache.ignite.ml.tree.randomforest	Contains random forest implementation classes.

Uses of ModelsComposition in org.apache.ignite.ml.composition

Methods in org.apache.ignite.ml.composition that return ModelsComposition

Modifier and Type	Method and Description
<K,V> ModelsComposition	BaggingModelTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Trains model based on the specified data.
<K,V> ModelsComposition	BaggingModelTrainer.updateModel(ModelsComposition mdl, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Learn new models on dataset and create new Compositions over them and already learned models.

Methods in org.apache.ignite.ml.composition with parameters of type ModelsComposition

Modifier and Type	Method and Description
<K,V> ModelsComposition	BaggingModelTrainer.updateModel(ModelsComposition mdl, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Learn new models on dataset and create new Compositions over them and already learned models.

Uses of ModelsComposition in org.apache.ignite.ml.composition.boosting

Subclasses of ModelsComposition in org.apache.ignite.ml.composition.boosting

Modifier and Type	Class and Description
static class	GDBTrainer.GDBModel GDB model.

Methods in org.apache.ignite.ml.composition.boosting that return ModelsComposition

Modifier and Type	Method and Description
<K,V> ModelsComposition	GDBTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Trains model based on the specified data.
protected <K,V> ModelsComposition	GDBTrainer.updateModel(ModelsComposition mdl, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Gets state of model in arguments, update in according to new data and return new model.

Methods in org.apache.ignite.ml.composition.boosting with parameters of type ModelsComposition

Modifier and Type	Method and Description
protected boolean	GDBTrainer.checkState(ModelsComposition mdl)
protected <K,V> ModelsComposition	GDBTrainer.updateModel(ModelsComposition mdl, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Gets state of model in arguments, update in according to new data and return new model.

Uses of ModelsComposition in org.apache.ignite.ml.composition.boosting.convergence

Methods in org.apache.ignite.ml.composition.boosting.convergence with parameters of type ModelsComposition

Modifier and Type	Method and Description
double	ConvergenceChecker.computeError(Vector features, Double answer, ModelsComposition currMdl) Compute error for the specific vector of dataset.
abstract Double	ConvergenceChecker.computeMeanErrorOnDataset(Dataset<EmptyContext,? extends FeatureMatrixWithLabelsOnHeapData> dataset, ModelsComposition mdl) Compute error for given model on learning dataset.
boolean	ConvergenceChecker.isConverged(Dataset<EmptyContext,? extends FeatureMatrixWithLabelsOnHeapData> dataset, ModelsComposition currMdl) Checks convergency on dataset.
boolean	ConvergenceChecker.isConverged(DatasetBuilder<K,V> datasetBuilder, ModelsComposition currMdl) Checks convergency on dataset.

Uses of ModelsComposition in org.apache.ignite.ml.composition.boosting.convergence.mean

Methods in org.apache.ignite.ml.composition.boosting.convergence.mean with parameters of type ModelsComposition

Modifier and Type	Method and Description
Double	MeanAbsValueConvergenceChecker.computeMeanErrorOnDataset(Dataset<EmptyContext,? extends FeatureMatrixWithLabelsOnHeapData> dataset, ModelsComposition mdl) Compute error for given model on learning dataset.

Uses of ModelsComposition in org.apache.ignite.ml.composition.boosting.convergence.median

Methods in org.apache.ignite.ml.composition.boosting.convergence.median with parameters of type ModelsComposition

Modifier and Type	Method and Description
Double	MedianOfMedianConvergenceChecker.computeMeanErrorOnDataset(Dataset<EmptyContext,? extends FeatureMatrixWithLabelsOnHeapData> dataset, ModelsComposition mdl) Compute error for given model on learning dataset.

Uses of ModelsComposition in org.apache.ignite.ml.composition.boosting.convergence.simple

Methods in org.apache.ignite.ml.composition.boosting.convergence.simple with parameters of type ModelsComposition

Modifier and Type	Method and Description
Double	ConvergenceCheckerStub.computeMeanErrorOnDataset(Dataset<EmptyContext,? extends FeatureMatrixWithLabelsOnHeapData> dataset, ModelsComposition mdl) Compute error for given model on learning dataset.
boolean	ConvergenceCheckerStub.isConverged(Dataset<EmptyContext,? extends FeatureMatrixWithLabelsOnHeapData> dataset, ModelsComposition currMdl) Checks convergency on dataset.
boolean	ConvergenceCheckerStub.isConverged(DatasetBuilder<K,V> datasetBuilder, ModelsComposition currMdl) Checks convergency on dataset.

Uses of ModelsComposition in org.apache.ignite.ml.tree.randomforest

Methods in org.apache.ignite.ml.tree.randomforest that return ModelsComposition

Modifier and Type	Method and Description
protected ModelsComposition	RandomForestRegressionTrainer.buildComposition(List<TreeRoot> models) Returns composition of built trees.
protected ModelsComposition	RandomForestClassifierTrainer.buildComposition(List<TreeRoot> models) Returns composition of built trees.
protected abstract ModelsComposition	RandomForestTrainer.buildComposition(List<TreeRoot> models) Returns composition of built trees.
<K,V> ModelsComposition	RandomForestTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Trains model based on the specified data.
protected <K,V> ModelsComposition	RandomForestTrainer.updateModel(ModelsComposition mdl, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Gets state of model in arguments, update in according to new data and return new model.

Methods in org.apache.ignite.ml.tree.randomforest with parameters of type ModelsComposition

Modifier and Type	Method and Description
protected boolean	RandomForestTrainer.checkState(ModelsComposition mdl)
protected <K,V> ModelsComposition	RandomForestTrainer.updateModel(ModelsComposition mdl, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Gets state of model in arguments, update in according to new data and return new model.