Uses of Interface
org.apache.ignite.ml.dataset.DatasetBuilder

Packages that use DatasetBuilder

Package	Description
org.apache.ignite.ml.clustering.kmeans	Contains kMeans clustering algorithm.
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.dataset	Base package for machine learning dataset classes.
org.apache.ignite.ml.dataset.impl.cache	Base package for cache based implementation of machine learning dataset.
org.apache.ignite.ml.dataset.impl.local	Base package for local implementation of machine learning dataset.
org.apache.ignite.ml.knn	Contains main APIs for kNN algorithms.
org.apache.ignite.ml.knn.ann	Contains main APIs for ANN classification algorithms.
org.apache.ignite.ml.knn.classification	Contains main APIs for kNN classification algorithms.
org.apache.ignite.ml.knn.regression	Contains helper classes for kNN regression algorithms.
org.apache.ignite.ml.math.isolve.lsqr	Contains LSQR algorithm implementation.
org.apache.ignite.ml.nn	Contains neural networks and related classes.
org.apache.ignite.ml.preprocessing	Base package for machine learning preprocessing classes.
org.apache.ignite.ml.preprocessing.binarization	Contains binarization preprocessor.
org.apache.ignite.ml.preprocessing.encoding	Contains encoding preprocessors.
org.apache.ignite.ml.preprocessing.imputing	Contains Imputer preprocessor.
org.apache.ignite.ml.preprocessing.maxabsscaling	Contains Max Abs Scaler preprocessor.
org.apache.ignite.ml.preprocessing.minmaxscaling	Contains Min Max Scaler preprocessor.
org.apache.ignite.ml.preprocessing.normalization	Contains Normalizer preprocessor.
org.apache.ignite.ml.regressions.linear	Contains various linear regressions.
org.apache.ignite.ml.regressions.logistic.binomial	Contains binomial logistic regression.
org.apache.ignite.ml.regressions.logistic.multiclass	Contains multi-class logistic regression.
org.apache.ignite.ml.svm	Contains main APIs for SVM(support vector machines) algorithms.
org.apache.ignite.ml.trainers	Contains model trainers.
org.apache.ignite.ml.tree	Root package for decision trees.
org.apache.ignite.ml.tree.boosting	Contains implementation of gradient boosting on trees.
org.apache.ignite.ml.tree.randomforest	Contains random forest implementation classes.

Uses of DatasetBuilder in org.apache.ignite.ml.clustering.kmeans

Methods in org.apache.ignite.ml.clustering.kmeans with parameters of type DatasetBuilder

Modifier and Type	Method and Description
<K,V> KMeansModel	KMeansTrainer.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> KMeansModel	KMeansTrainer.updateModel(KMeansModel 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 DatasetBuilder in org.apache.ignite.ml.composition

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

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.

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

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

Modifier and Type	Method and Description
protected <V,K> IgniteBiTuple<Double,Long>	GDBTrainer.computeInitialValue(DatasetBuilder<K,V> builder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Compute mean value of label as first approximation.
<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 <V,K> boolean	GDBBinaryClassifierTrainer.learnLabels(DatasetBuilder<K,V> builder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lExtractor) Defines unique labels in dataset if need (useful in case of classification).
protected abstract <V,K> boolean	GDBTrainer.learnLabels(DatasetBuilder<K,V> builder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lExtractor) Defines unique labels in dataset if need (useful in case of classification).
protected <V,K> boolean	GDBRegressionTrainer.learnLabels(DatasetBuilder<K,V> builder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lExtractor) Defines unique labels in dataset if need (useful in case of classification).
<K,V> List<Model<Vector,Double>>	GDBLearningStrategy.learnModels(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Implementation of gradient boosting iterations.
<K,V> List<Model<Vector,Double>>	GDBLearningStrategy.update(GDBTrainer.GDBModel mdlToUpdate, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Gets state of model in arguments, compare it with training parameters of trainer and if they are fit then trainer updates model in according to new data and return new model.
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 DatasetBuilder in org.apache.ignite.ml.composition.boosting.convergence

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

Modifier and Type	Method and Description
abstract <K,V> ConvergenceChecker<K,V>	ConvergenceCheckerFactory.create(long sampleSize, IgniteFunction<Double,Double> externalLbToInternalMapping, Loss loss, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Create an instance of ConvergenceChecker.
boolean	ConvergenceChecker.isConverged(DatasetBuilder<K,V> datasetBuilder, ModelsComposition currMdl) Checks convergency on dataset.

Constructors in org.apache.ignite.ml.composition.boosting.convergence with parameters of type DatasetBuilder

Constructor and Description
ConvergenceChecker(long sampleSize, IgniteFunction<Double,Double> externalLbToInternalMapping, Loss loss, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor, double precision) Constructs an instance of ConvergenceChecker.

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

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

Modifier and Type	Method and Description
<K,V> ConvergenceChecker<K,V>	MeanAbsValueConvergenceCheckerFactory.create(long sampleSize, IgniteFunction<Double,Double> externalLbToInternalMapping, Loss loss, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Create an instance of ConvergenceChecker.

Constructors in org.apache.ignite.ml.composition.boosting.convergence.mean with parameters of type DatasetBuilder

Constructor and Description
MeanAbsValueConvergenceChecker(long sampleSize, IgniteFunction<Double,Double> externalLbToInternalMapping, Loss loss, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor, double precision) Creates an intance of MeanAbsValueConvergenceChecker.

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

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

Modifier and Type	Method and Description
<K,V> ConvergenceChecker<K,V>	MedianOfMedianConvergenceCheckerFactory.create(long sampleSize, IgniteFunction<Double,Double> externalLbToInternalMapping, Loss loss, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Create an instance of ConvergenceChecker.

Constructors in org.apache.ignite.ml.composition.boosting.convergence.median with parameters of type DatasetBuilder

Constructor and Description
MedianOfMedianConvergenceChecker(long sampleSize, IgniteFunction<Double,Double> lblMapping, Loss loss, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> fExtr, IgniteBiFunction<K,V,Double> lbExtr, double precision) Creates an instance of MedianOfMedianConvergenceChecker.

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

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

Modifier and Type	Method and Description
<K,V> ConvergenceChecker<K,V>	ConvergenceCheckerStubFactory.create(long sampleSize, IgniteFunction<Double,Double> externalLbToInternalMapping, Loss loss, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Create an instance of ConvergenceChecker.
boolean	ConvergenceCheckerStub.isConverged(DatasetBuilder<K,V> datasetBuilder, ModelsComposition currMdl) Checks convergency on dataset.

Constructors in org.apache.ignite.ml.composition.boosting.convergence.simple with parameters of type DatasetBuilder

Constructor and Description
ConvergenceCheckerStub(long sampleSize, IgniteFunction<Double,Double> externalLbToInternalMapping, Loss loss, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Creates an intance of ConvergenceCheckerStub.

Uses of DatasetBuilder in org.apache.ignite.ml.dataset

Methods in org.apache.ignite.ml.dataset that return DatasetBuilder

Modifier and Type	Method and Description
DatasetBuilder<K,V>	DatasetBuilder.withFilter(IgniteBiPredicate<K,V> filterToAdd) Returns new instance of DatasetBuilder using conjunction of internal filter and filterToAdd.

Methods in org.apache.ignite.ml.dataset with parameters of type DatasetBuilder

Modifier and Type	Method and Description
static <K,V,C extends Serializable,D extends AutoCloseable> Dataset<C,D>	DatasetFactory.create(DatasetBuilder<K,V> datasetBuilder, PartitionContextBuilder<K,V,C> partCtxBuilder, PartitionDataBuilder<K,V,C,D> partDataBuilder) Creates a new instance of distributed dataset using the specified partCtxBuilder and partDataBuilder.
static <K,V> SimpleDataset<EmptyContext>	DatasetFactory.createSimpleDataset(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor) Creates a new instance of distributed SimpleDataset using the specified featureExtractor.
static <K,V,C extends Serializable> SimpleDataset<C>	DatasetFactory.createSimpleDataset(DatasetBuilder<K,V> datasetBuilder, PartitionContextBuilder<K,V,C> partCtxBuilder, IgniteBiFunction<K,V,Vector> featureExtractor) Creates a new instance of distributed SimpleDataset using the specified partCtxBuilder and featureExtractor.
static <K,V> SimpleLabeledDataset<EmptyContext>	DatasetFactory.createSimpleLabeledDataset(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,double[]> lbExtractor) Creates a new instance of distributed SimpleLabeledDataset using the specified featureExtractor and lbExtractor.
static <K,V,C extends Serializable> SimpleLabeledDataset<C>	DatasetFactory.createSimpleLabeledDataset(DatasetBuilder<K,V> datasetBuilder, PartitionContextBuilder<K,V,C> partCtxBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,double[]> lbExtractor) Creates a new instance of distributed SimpleLabeledDataset using the specified partCtxBuilder, featureExtractor and lbExtractor.

Uses of DatasetBuilder in org.apache.ignite.ml.dataset.impl.cache

Classes in org.apache.ignite.ml.dataset.impl.cache that implement DatasetBuilder

Modifier and Type	Class and Description
class	CacheBasedDatasetBuilder<K,V> A dataset builder that makes CacheBasedDataset.

Methods in org.apache.ignite.ml.dataset.impl.cache that return DatasetBuilder

Modifier and Type	Method and Description
DatasetBuilder<K,V>	CacheBasedDatasetBuilder.withFilter(IgniteBiPredicate<K,V> filterToAdd) Returns new instance of DatasetBuilder using conjunction of internal filter and filterToAdd.

Uses of DatasetBuilder in org.apache.ignite.ml.dataset.impl.local

Classes in org.apache.ignite.ml.dataset.impl.local that implement DatasetBuilder

Modifier and Type	Class and Description
class	LocalDatasetBuilder<K,V> A dataset builder that makes LocalDataset.

Methods in org.apache.ignite.ml.dataset.impl.local that return DatasetBuilder

Modifier and Type	Method and Description
DatasetBuilder<K,V>	LocalDatasetBuilder.withFilter(IgniteBiPredicate<K,V> filterToAdd) Returns new instance of DatasetBuilder using conjunction of internal filter and filterToAdd.

Uses of DatasetBuilder in org.apache.ignite.ml.knn

Methods in org.apache.ignite.ml.knn with parameters of type DatasetBuilder

Modifier and Type	Method and Description
static <K,V> @Nullable Dataset<EmptyContext,LabeledVectorSet<Double,LabeledVector>>	KNNUtils.buildDataset(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Builds dataset.

Uses of DatasetBuilder in org.apache.ignite.ml.knn.ann

Methods in org.apache.ignite.ml.knn.ann with parameters of type DatasetBuilder

Modifier and Type	Method and Description
<K,V> ANNClassificationModel	ANNClassificationTrainer.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> ANNClassificationModel	ANNClassificationTrainer.updateModel(ANNClassificationModel 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 DatasetBuilder in org.apache.ignite.ml.knn.classification

Methods in org.apache.ignite.ml.knn.classification with parameters of type DatasetBuilder

Modifier and Type	Method and Description
<K,V> KNNClassificationModel	KNNClassificationTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Trains model based on the specified data.
<K,V> KNNClassificationModel	KNNClassificationTrainer.updateModel(KNNClassificationModel 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 DatasetBuilder in org.apache.ignite.ml.knn.regression

Methods in org.apache.ignite.ml.knn.regression with parameters of type DatasetBuilder

Modifier and Type	Method and Description
<K,V> KNNRegressionModel	KNNRegressionTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Trains model based on the specified data.
<K,V> KNNRegressionModel	KNNRegressionTrainer.updateModel(KNNRegressionModel 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 DatasetBuilder in org.apache.ignite.ml.math.isolve.lsqr

Constructors in org.apache.ignite.ml.math.isolve.lsqr with parameters of type DatasetBuilder

Constructor and Description
LSQROnHeap(DatasetBuilder<K,V> datasetBuilder, PartitionDataBuilder<K,V,LSQRPartitionContext,SimpleLabeledDatasetData> partDataBuilder) Constructs a new instance of OnHeap LSQR algorithm implementation.

Uses of DatasetBuilder in org.apache.ignite.ml.nn

Methods in org.apache.ignite.ml.nn with parameters of type DatasetBuilder

Modifier and Type	Method and Description
<K,V> MultilayerPerceptron	MLPTrainer.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> MultilayerPerceptron	MLPTrainer.updateModel(MultilayerPerceptron lastLearnedMdl, 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 DatasetBuilder in org.apache.ignite.ml.preprocessing

Methods in org.apache.ignite.ml.preprocessing with parameters of type DatasetBuilder

Modifier and Type	Method and Description
IgniteBiFunction<K,V,R>	PreprocessingTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,T> basePreprocessor) Fits preprocessor.

Uses of DatasetBuilder in org.apache.ignite.ml.preprocessing.binarization

Methods in org.apache.ignite.ml.preprocessing.binarization with parameters of type DatasetBuilder

Modifier and Type	Method and Description
BinarizationPreprocessor<K,V>	BinarizationTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> basePreprocessor) Fits preprocessor.

Uses of DatasetBuilder in org.apache.ignite.ml.preprocessing.encoding

Methods in org.apache.ignite.ml.preprocessing.encoding with parameters of type DatasetBuilder

Modifier and Type	Method and Description
EncoderPreprocessor<K,V>	EncoderTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Object[]> basePreprocessor) Fits preprocessor.

Uses of DatasetBuilder in org.apache.ignite.ml.preprocessing.imputing

Methods in org.apache.ignite.ml.preprocessing.imputing with parameters of type DatasetBuilder

Modifier and Type	Method and Description
ImputerPreprocessor<K,V>	ImputerTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> basePreprocessor) Fits preprocessor.

Uses of DatasetBuilder in org.apache.ignite.ml.preprocessing.maxabsscaling

Methods in org.apache.ignite.ml.preprocessing.maxabsscaling with parameters of type DatasetBuilder

Modifier and Type	Method and Description
MaxAbsScalerPreprocessor<K,V>	MaxAbsScalerTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> basePreprocessor) Fits preprocessor.

Uses of DatasetBuilder in org.apache.ignite.ml.preprocessing.minmaxscaling

Methods in org.apache.ignite.ml.preprocessing.minmaxscaling with parameters of type DatasetBuilder

Modifier and Type	Method and Description
MinMaxScalerPreprocessor<K,V>	MinMaxScalerTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> basePreprocessor) Fits preprocessor.

Uses of DatasetBuilder in org.apache.ignite.ml.preprocessing.normalization

Methods in org.apache.ignite.ml.preprocessing.normalization with parameters of type DatasetBuilder

Modifier and Type	Method and Description
NormalizationPreprocessor<K,V>	NormalizationTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> basePreprocessor) Fits preprocessor.

Uses of DatasetBuilder in org.apache.ignite.ml.regressions.linear

Methods in org.apache.ignite.ml.regressions.linear with parameters of type DatasetBuilder

Modifier and Type	Method and Description
<K,V> LinearRegressionModel	LinearRegressionLSQRTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Trains model based on the specified data.
<K,V> LinearRegressionModel	LinearRegressionSGDTrainer.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> LinearRegressionModel	LinearRegressionLSQRTrainer.updateModel(LinearRegressionModel 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.
protected <K,V> LinearRegressionModel	LinearRegressionSGDTrainer.updateModel(LinearRegressionModel 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 DatasetBuilder in org.apache.ignite.ml.regressions.logistic.binomial

Methods in org.apache.ignite.ml.regressions.logistic.binomial with parameters of type DatasetBuilder

Modifier and Type	Method and Description
<K,V> LogisticRegressionModel	LogisticRegressionSGDTrainer.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> LogisticRegressionModel	LogisticRegressionSGDTrainer.updateModel(LogisticRegressionModel 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 DatasetBuilder in org.apache.ignite.ml.regressions.logistic.multiclass

Methods in org.apache.ignite.ml.regressions.logistic.multiclass with parameters of type DatasetBuilder

Modifier and Type	Method and Description
<K,V> LogRegressionMultiClassModel	LogRegressionMultiClassTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Trains model based on the specified data.
<K,V> LogRegressionMultiClassModel	LogRegressionMultiClassTrainer.updateModel(LogRegressionMultiClassModel newMdl, 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 DatasetBuilder in org.apache.ignite.ml.svm

Methods in org.apache.ignite.ml.svm with parameters of type DatasetBuilder

Modifier and Type	Method and Description
<K,V> SVMLinearMultiClassClassificationModel	SVMLinearMultiClassClassificationTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Trains model based on the specified data.
<K,V> SVMLinearBinaryClassificationModel	SVMLinearBinaryClassificationTrainer.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> SVMLinearBinaryClassificationModel	SVMLinearBinaryClassificationTrainer.updateModel(SVMLinearBinaryClassificationModel 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.
<K,V> SVMLinearMultiClassClassificationModel	SVMLinearMultiClassClassificationTrainer.updateModel(SVMLinearMultiClassClassificationModel 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 DatasetBuilder in org.apache.ignite.ml.trainers

Methods in org.apache.ignite.ml.trainers with parameters of type DatasetBuilder

Modifier and Type	Method and Description
abstract <K,V> M	DatasetTrainer.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor) Trains model based on the specified data.
<K,V> M	DatasetTrainer.update(M mdl, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor) Gets state of model in arguments, compare it with training parameters of trainer and if they are fit then trainer updates model in according to new data and return new model.
protected abstract <K,V> M	DatasetTrainer.updateModel(M mdl, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,L> lbExtractor) Gets state of model in arguments, update in according to new data and return new model.

Uses of DatasetBuilder in org.apache.ignite.ml.tree

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

Modifier and Type	Method and Description
<K,V> DecisionTreeNode	DecisionTree.fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Trains model based on the specified data.
<K,V> DecisionTreeNode	DecisionTree.updateModel(DecisionTreeNode mdl, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Trains new model based on dataset because there is no valid approach to update decision trees.

Uses of DatasetBuilder in org.apache.ignite.ml.tree.boosting

Methods in org.apache.ignite.ml.tree.boosting with parameters of type DatasetBuilder

Modifier and Type	Method and Description
<K,V> List<Model<Vector,Double>>	GDBOnTreesLearningStrategy.update(GDBTrainer.GDBModel mdlToUpdate, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Gets state of model in arguments, compare it with training parameters of trainer and if they are fit then trainer updates model in according to new data and return new model.

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

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

Modifier and Type	Method and Description
<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.