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

- Packages that use DatasetBuilder
  Package | Description
  --- | ---
  org.apache.ignite.ml.clustering.gmm | Contains Gauss Mixture Model clustering algorithm (see `GmmModel`).
  org.apache.ignite.ml.clustering.kmeans | Contains kMeans clustering algorithm.
  org.apache.ignite.ml.composition.bagging |
  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 convergence 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.composition.combinators.parallel | Contains parallel combiners (ensembles) of trainers and models.
  org.apache.ignite.ml.composition.combinators.sequential | Contains sequential combinators of trainers and models.
  org.apache.ignite.ml.composition.stacking | Contains classes used for training with stacking technique.
  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.utils | Contains util functionality for kNN algorithms.
  org.apache.ignite.ml.math.isolve.lsqr | Contains LSQR algorithm implementation.
  org.apache.ignite.ml.multiclass | Contains various multi-classifier models and trainers.
  org.apache.ignite.ml.naivebayes.compound | Contains Compound naive Bayes classifier.
  org.apache.ignite.ml.naivebayes.discrete | Contains Bernoulli naive Bayes classifier.
  org.apache.ignite.ml.naivebayes.gaussian | Contains Gaussian naive Bayes classifier.
  org.apache.ignite.ml.nn | Contains neural networks and related classes.
  org.apache.ignite.ml.pipeline | Contains Pipeline API.
  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.finalization | Contains vector finalization preprocessor.
  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.preprocessing.standardscaling | Contains Standard scaler preprocessor.
  org.apache.ignite.ml.recommendation | Contains recommendation system framework.
  org.apache.ignite.ml.regressions.linear | Contains various linear regressions.
  org.apache.ignite.ml.regressions.logistic | Contains various logistic regressions.
  org.apache.ignite.ml.selection.cv | Root package for cross-validation algorithms.
  org.apache.ignite.ml.selection.scoring.evaluator | Package for model evaluator classes.
  org.apache.ignite.ml.sql | Contains util classes that help to work with machine learning models in SQL and train models on data from SQL tables.
  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.
  org.apache.ignite.ml.util.generators | Contains utility classes for data streams generation.

- - ### Uses of DatasetBuilder in org.apache.ignite.ml.clustering.gmm

    Methods in org.apache.ignite.ml.clustering.gmm with parameters of type DatasetBuilder
    Modifier and Type | Method and Description
    --- | ---
    `<K,V> GmmModel` | GmmTrainer.`fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> extractor)` Trains model based on the specified data.
    `protected <K,V> GmmModel` | GmmTrainer.`updateModel(GmmModel mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> extractor)` Gets state of model in arguments, update in according to new data and return new model.

  - ### 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.`fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor)` Trains model based on the specified data.
    `protected <K,V> KMeansModel` | KMeansTrainer.`updateModel(KMeansModel mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor)` 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.bagging

    Methods in org.apache.ignite.ml.composition.bagging with parameters of type DatasetBuilder
    Modifier and Type | Method and Description
    --- | ---
    `<K,V> BaggedModel` | BaggedTrainer.`fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor)` Trains model based on the specified data.
    `<K,V> BaggedModel` | BaggedTrainer.`update(BaggedModel mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor)` 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> BaggedModel` | BaggedTrainer.`updateModel(BaggedModel mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor)` This method is never called, instead of constructing logic of update from `DatasetTrainer.isUpdateable(M)` and `DatasetTrainer.updateModel(M, org.apache.ignite.ml.dataset.DatasetBuilder<K, V>, org.apache.ignite.ml.preprocessing.Preprocessor<K, V>)` in this class we explicitly override update method.

  - ### 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,C extends Serializable> IgniteBiTuple<Double,Long>` | GDBTrainer.`computeInitialValue(LearningEnvironmentBuilder envBuilder, DatasetBuilder<K,V> builder, Preprocessor<K,V> preprocessor)` Compute mean value of label as first approximation.
    `<K,V> ModelsComposition` | GDBTrainer.`fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor)` Trains model based on the specified data.
    `protected <V,K> boolean` | GDBBinaryClassifierTrainer.`learnLabels(DatasetBuilder<K,V> builder, Preprocessor<K,V> preprocessor)` Defines unique labels in dataset if need (useful in case of classification).
    `protected abstract <V,K> boolean` | GDBTrainer.`learnLabels(DatasetBuilder<K,V> builder, Preprocessor<K,V> preprocessor)` Defines unique labels in dataset if need (useful in case of classification).
    `protected <V,K> boolean` | GDBRegressionTrainer.`learnLabels(DatasetBuilder<K,V> builder, Preprocessor<K,V> preprocessor)` Defines unique labels in dataset if need (useful in case of classification).
    `<K,V> List<IgniteModel<Vector,Double>>` | GDBLearningStrategy.`learnModels(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor)` Implementation of gradient boosting iterations.
    `<K,V> List<IgniteModel<Vector,Double>>` | GDBLearningStrategy.`update(GDBTrainer.GDBModel mdlToUpdate, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor)` 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, Preprocessor<K,V> preprocessor)` 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, Preprocessor<K,V> vectorizer)` Create an instance of ConvergenceChecker.
    `boolean` | ConvergenceChecker.`isConverged(LearningEnvironmentBuilder envBuilder, 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, Preprocessor<K,V> preprocessor, 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, Preprocessor<K,V> preprocessor)` 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, Preprocessor<K,V> preprocessor, double precision)` Creates an instance 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, Preprocessor<K,V> preprocessor)` 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, Preprocessor<K,V> preprocessor, 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, Preprocessor<K,V> preprocessor)` Create an instance of ConvergenceChecker.
    `boolean` | ConvergenceCheckerStub.`isConverged(LearningEnvironmentBuilder envBuilder, 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 externalLbToInternalMapping, Loss loss, DatasetBuilder datasetBuilder, Preprocessor<K,V> preprocessor, double precision)` Creates an instance of ConvergenceCheckerStub.

  - ### Uses of DatasetBuilder in org.apache.ignite.ml.composition.combinators.parallel

    Methods in org.apache.ignite.ml.composition.combinators.parallel with parameters of type DatasetBuilder
    Modifier and Type | Method and Description
    --- | ---
    `<K,V> IgniteModel<I,List<O>>` | TrainersParallelComposition.`fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor)` Trains model based on the specified data.
    `<K,V> IgniteModel<I,List<O>>` | TrainersParallelComposition.`update(IgniteModel<I,List<O>> mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor)` 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> IgniteModel<I,List<O>>` | TrainersParallelComposition.`updateModel(IgniteModel<I,List<O>> mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor)` This method is never called, instead of constructing logic of update from `DatasetTrainer.isUpdateable(IgniteModel)` and `DatasetTrainer.updateModel(IgniteModel, DatasetBuilder, Preprocessor)` in this class we explicitly override update method.

  - ### Uses of DatasetBuilder in org.apache.ignite.ml.composition.combinators.sequential

    Methods in org.apache.ignite.ml.composition.combinators.sequential with parameters of type DatasetBuilder
    Modifier and Type | Method and Description
    --- | ---
    `<K,V> ModelsSequentialComposition<I,O1,O2>` | TrainersSequentialComposition.`fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor)` Trains model based on the specified data.
    `<K,V> ModelsSequentialComposition<I,O1,O2>` | TrainersSequentialComposition.`update(ModelsSequentialComposition<I,O1,O2> mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor)` 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> ModelsSequentialComposition<I,O1,O2>` | TrainersSequentialComposition.`updateModel(ModelsSequentialComposition<I,O1,O2> mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor)` This method is never called, instead of constructing logic of update from `DatasetTrainer.isUpdateable(IgniteModel)` and `DatasetTrainer.updateModel(IgniteModel, DatasetBuilder, Preprocessor)` in this class we explicitly override update method.

  - ### Uses of DatasetBuilder in org.apache.ignite.ml.composition.stacking

    Methods in org.apache.ignite.ml.composition.stacking with parameters of type DatasetBuilder
    Modifier and Type | Method and Description
    --- | ---
    `<K,V> StackedModel<IS,IA,O,AM>` | StackedDatasetTrainer.`fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor)` Trains model based on the specified data.
    `<K,V> StackedModel<IS,IA,O,AM>` | StackedDatasetTrainer.`update(StackedModel<IS,IA,O,AM> mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor)` 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> StackedModel<IS,IA,O,AM>` | StackedDatasetTrainer.`updateModel(StackedModel<IS,IA,O,AM> mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor)` This method is never called, instead of constructing logic of update from `DatasetTrainer.isUpdateable(IgniteModel)` and `DatasetTrainer.updateModel(IgniteModel, DatasetBuilder, Preprocessor)` in this class we explicitly override update method.

  - ### 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`.
    `DatasetBuilder<K,V>` | DatasetBuilder.`withUpstreamTransformer(UpstreamTransformerBuilder builder)` Returns new instance of `DatasetBuilder` with new `UpstreamTransformerBuilder` added to chain of upstream transformer builders.

    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, LearningEnvironmentBuilder envBuilder, PartitionContextBuilder<K,V,C> partCtxBuilder, PartitionDataBuilder<K,V,C,D> partDataBuilder, LearningEnvironment environment)` Creates a new instance of distributed dataset using the specified `partCtxBuilder` and `partDataBuilder`.
    `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,C extends Serializable,CO extends Serializable> SimpleDataset<C>` | DatasetFactory.`createSimpleDataset(DatasetBuilder<K,V> datasetBuilder, LearningEnvironmentBuilder envBuilder, PartitionContextBuilder<K,V,C> partCtxBuilder, Preprocessor<K,V> featureExtractor)` Creates a new instance of distributed `SimpleDataset` using the specified `partCtxBuilder` and `featureExtractor`.
    `static <K,V,CO extends Serializable> SimpleDataset<EmptyContext>` | DatasetFactory.`createSimpleDataset(DatasetBuilder<K,V> datasetBuilder, LearningEnvironmentBuilder envBuilder, Preprocessor<K,V> featureExtractor)` Creates a new instance of distributed `SimpleDataset` using the specified `featureExtractor`.
    `static <K,V,C extends Serializable,CO extends Serializable> SimpleLabeledDataset<C>` | DatasetFactory.`createSimpleLabeledDataset(DatasetBuilder<K,V> datasetBuilder, LearningEnvironmentBuilder envBuilder, PartitionContextBuilder<K,V,C> partCtxBuilder, Preprocessor<K,V> vectorizer)` Creates a new instance of distributed `SimpleLabeledDataset` using the specified `partCtxBuilder`, `featureExtractor` and `lbExtractor`.
    `static <K,V,CO extends Serializable> SimpleLabeledDataset<EmptyContext>` | DatasetFactory.`createSimpleLabeledDataset(DatasetBuilder<K,V> datasetBuilder, LearningEnvironmentBuilder envBuilder, Preprocessor<K,V> vectorizer)` Creates a new instance of distributed `SimpleLabeledDataset` using the specified `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`.
    `DatasetBuilder<K,V>` | CacheBasedDatasetBuilder.`withUpstreamTransformer(UpstreamTransformerBuilder builder)` Returns new instance of `DatasetBuilder` with new `UpstreamTransformerBuilder` added to chain of upstream transformer builders.

  - ### 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`.
    `DatasetBuilder<K,V>` | LocalDatasetBuilder.`withUpstreamTransformer(UpstreamTransformerBuilder builder)` Returns new instance of `DatasetBuilder` with new `UpstreamTransformerBuilder` added to chain of upstream transformer builders.

  - ### 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
    --- | ---
    `protected <K,V> M` | KNNTrainer.`fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor)` Trains model based on the specified data.
    `protected <K,V> M` | KNNTrainer.`updateModel(M mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor)` 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.ann

    Methods in org.apache.ignite.ml.knn.ann with parameters of type DatasetBuilder
    Modifier and Type | Method and Description
    --- | ---
    `<K,V> ANNClassificationModel` | ANNClassificationTrainer.`fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> extractor)` Trains model based on the specified data.
    `protected <K,V> ANNClassificationModel` | ANNClassificationTrainer.`updateModel(ANNClassificationModel mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> extractor)` 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.utils

    Methods in org.apache.ignite.ml.knn.utils with parameters of type DatasetBuilder
    Modifier and Type | Method and Description
    --- | ---
    `static <K,V,C extends Serializable> @Nullable Dataset<EmptyContext,LabeledVectorSet<LabeledVector>>` | KNNUtils.`buildDataset(LearningEnvironmentBuilder envBuilder, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> vectorizer)` Builds dataset.

  - ### 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, LearningEnvironmentBuilder envBuilder, PartitionDataBuilder<K,V,LSQRPartitionContext,SimpleLabeledDatasetData> partDataBuilder, LearningEnvironment localLearningEnv)` Constructs a new instance of OnHeap LSQR algorithm implementation.

  - ### Uses of DatasetBuilder in org.apache.ignite.ml.multiclass

    Methods in org.apache.ignite.ml.multiclass with parameters of type DatasetBuilder
    Modifier and Type | Method and Description
    --- | ---
    `<K,V> MultiClassModel<M>` | OneVsRestTrainer.`fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> extractor)` Trains model based on the specified data.
    `protected <K,V> MultiClassModel<M>` | OneVsRestTrainer.`updateModel(MultiClassModel<M> newMdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> extractor)` Gets state of model in arguments, update in according to new data and return new model.

  - ### Uses of DatasetBuilder in org.apache.ignite.ml.naivebayes.compound

    Methods in org.apache.ignite.ml.naivebayes.compound with parameters of type DatasetBuilder
    Modifier and Type | Method and Description
    --- | ---
    `<K,V> CompoundNaiveBayesModel` | CompoundNaiveBayesTrainer.`fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> extractor)` Trains model based on the specified data.
    `protected <K,V> CompoundNaiveBayesModel` | CompoundNaiveBayesTrainer.`updateModel(CompoundNaiveBayesModel mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> extractor)` Gets state of model in arguments, update in according to new data and return new model.

  - ### Uses of DatasetBuilder in org.apache.ignite.ml.naivebayes.discrete

    Methods in org.apache.ignite.ml.naivebayes.discrete with parameters of type DatasetBuilder
    Modifier and Type | Method and Description
    --- | ---
    `<K,V> DiscreteNaiveBayesModel` | DiscreteNaiveBayesTrainer.`fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> extractor)` Trains model based on the specified data.
    `protected <K,V> DiscreteNaiveBayesModel` | DiscreteNaiveBayesTrainer.`updateModel(DiscreteNaiveBayesModel mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> extractor)` Gets state of model in arguments, update in according to new data and return new model.

  - ### Uses of DatasetBuilder in org.apache.ignite.ml.naivebayes.gaussian

    Methods in org.apache.ignite.ml.naivebayes.gaussian with parameters of type DatasetBuilder
    Modifier and Type | Method and Description
    --- | ---
    `<K,V> GaussianNaiveBayesModel` | GaussianNaiveBayesTrainer.`fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> extractor)` Trains model based on the specified data.
    `protected <K,V> GaussianNaiveBayesModel` | GaussianNaiveBayesTrainer.`updateModel(GaussianNaiveBayesModel mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> extractor)` Gets state of model in arguments, update in according to new data and return new model.

  - ### 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.`fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> extractor)` Trains model based on the specified data.
    `protected <K,V> MultilayerPerceptron` | MLPTrainer.`updateModel(MultilayerPerceptron lastLearnedMdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> extractor)` Gets state of model in arguments, update in according to new data and return new model.

  - ### Uses of DatasetBuilder in org.apache.ignite.ml.pipeline

    Methods in org.apache.ignite.ml.pipeline with parameters of type DatasetBuilder
    Modifier and Type | Method and Description
    --- | ---
    `PipelineMdl<K,V>` | Pipeline.`fit(DatasetBuilder datasetBuilder)` Fits the pipeline to the input dataset builder.

  - ### 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
    --- | ---
    `default Preprocessor<K,V>` | PreprocessingTrainer.`fit(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> basePreprocessor)` Fits preprocessor.
    `Preprocessor<K,V>` | PreprocessingTrainer.`fit(LearningEnvironmentBuilder envBuilder, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> 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(LearningEnvironmentBuilder envBuilder, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> 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(LearningEnvironmentBuilder envBuilder, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> basePreprocessor)` Fits preprocessor.

  - ### Uses of DatasetBuilder in org.apache.ignite.ml.preprocessing.finalization

    Methods in org.apache.ignite.ml.preprocessing.finalization with parameters of type DatasetBuilder
    Modifier and Type | Method and Description
    --- | ---
    `VectorFinalizationPreprocessor<K,V>` | VectorFinalizationTrainer.`fit(LearningEnvironmentBuilder envBuilder, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> 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(LearningEnvironmentBuilder envBuilder, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> 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(LearningEnvironmentBuilder envBuilder, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> 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(LearningEnvironmentBuilder envBuilder, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> 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(LearningEnvironmentBuilder envBuilder, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> basePreprocessor)` Fits preprocessor.

  - ### Uses of DatasetBuilder in org.apache.ignite.ml.preprocessing.standardscaling

    Methods in org.apache.ignite.ml.preprocessing.standardscaling with parameters of type DatasetBuilder
    Modifier and Type | Method and Description
    --- | ---
    `StandardScalerPreprocessor<K,V>` | StandardScalerTrainer.`fit(LearningEnvironmentBuilder envBuilder, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> basePreprocessor)` Fits preprocessor.

  - ### Uses of DatasetBuilder in org.apache.ignite.ml.recommendation

    Methods in org.apache.ignite.ml.recommendation with parameters of type DatasetBuilder
    Modifier and Type | Method and Description
    --- | ---
    `<K,O extends Serializable,S extends Serializable> RecommendationModel<O,S>` | RecommendationTrainer.`fit(DatasetBuilder<K,? extends ObjectSubjectRatingTriplet<O,S>> datasetBuilder)` Fits prediction model.
    `RecommendationModel<Serializable,Serializable>` | RecommendationTrainer.`fit(DatasetBuilder<Object,BinaryObject> datasetBuilder, String objFieldName, String subjFieldName, String ratingFieldName)` Fits prediction model on a data stored in binary format.
    `<K,O extends Serializable,S extends Serializable> RecommendationModel<O,S>` | RecommendationTrainer.`update(DatasetBuilder<K,? extends ObjectSubjectRatingTriplet<O,S>> datasetBuilder, RecommendationModel<O,S> mdl)` Fits prediction model.
    `RecommendationModel<Serializable,Serializable>` | RecommendationTrainer.`update(DatasetBuilder<Object,BinaryObject> datasetBuilder, String objFieldName, String subjFieldName, String ratingFieldName, RecommendationModel<Serializable,Serializable> mdl)` Updates prediction model on a data stored in binary format.

  - ### 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.`fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> extractor)` Trains model based on the specified data.
    `<K,V> LinearRegressionModel` | LinearRegressionSGDTrainer.`fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> extractor)` Trains model based on the specified data.
    `protected <K,V> LinearRegressionModel` | LinearRegressionLSQRTrainer.`updateModel(LinearRegressionModel mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> extractor)` 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, Preprocessor<K,V> extractor)` 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

    Methods in org.apache.ignite.ml.regressions.logistic with parameters of type DatasetBuilder
    Modifier and Type | Method and Description
    --- | ---
    `<K,V> LogisticRegressionModel` | LogisticRegressionSGDTrainer.`fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> extractor)` Trains model based on the specified data.
    `protected <K,V> LogisticRegressionModel` | LogisticRegressionSGDTrainer.`updateModel(LogisticRegressionModel mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> extractor)` Gets state of model in arguments, update in according to new data and return new model.

  - ### Uses of DatasetBuilder in org.apache.ignite.ml.selection.cv

    Method parameters in org.apache.ignite.ml.selection.cv with type arguments of type DatasetBuilder
    Modifier and Type | Method and Description
    --- | ---
    `protected double[]` | AbstractCrossValidation.`score(Function<IgniteBiPredicate<K,V>,DatasetBuilder<K,V>> datasetBuilderSupplier)` Computes cross-validated metrics.
    `protected double[]` | AbstractCrossValidation.`scorePipeline(Function<IgniteBiPredicate<K,V>,DatasetBuilder<K,V>> datasetBuilderSupplier)` Computes cross-validated metrics.

  - ### Uses of DatasetBuilder in org.apache.ignite.ml.selection.scoring.evaluator

    Methods in org.apache.ignite.ml.selection.scoring.evaluator with parameters of type DatasetBuilder
    Modifier and Type | Method and Description
    --- | ---
    `static <K,V> EvaluationResult` | Evaluator.`evaluate(DatasetBuilder<K,V> datasetBuilder, IgniteModel<Vector,Double> mdl, Preprocessor<K,V> preprocessor, Metric... metrics)` Evaluate model.

  - ### Uses of DatasetBuilder in org.apache.ignite.ml.sql

    Classes in org.apache.ignite.ml.sql that implement DatasetBuilder
    Modifier and Type | Class and Description
    --- | ---
    `class` | `SqlDatasetBuilder` Subclass of `CacheBasedDatasetBuilder` that should be used to work with SQL tables.

  - ### 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> SVMLinearClassificationModel` | SVMLinearClassificationTrainer.`fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor)` Trains model based on the specified data.
    `protected <K,V> SVMLinearClassificationModel` | SVMLinearClassificationTrainer.`updateModel(SVMLinearClassificationModel mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor)` 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
    --- | ---
    `<K,V> M` | DatasetTrainer.`fit(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor)` Trains model based on the specified data.
    `<K,V> M` | DatasetTrainer.`fit(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor, LearningEnvironment learningEnvironment)` Trains model based on the specified data.
    `<K,V> AdaptableDatasetModel<I,O,IW,OW,M>` | AdaptableDatasetTrainer.`fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> extractor)` Trains model based on the specified data.
    `protected abstract <K,V> M` | DatasetTrainer.`fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor)` Trains model based on the specified data.
    `<K,V> M` | DatasetTrainer.`update(M mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor)` 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> AdaptableDatasetModel<I,O,IW,OW,M>` | AdaptableDatasetTrainer.`updateModel(AdaptableDatasetModel<I,O,IW,OW,M> mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> extractor)` Gets state of model in arguments, update in according to new data and return new model.
    `protected abstract <K,V> M` | DatasetTrainer.`updateModel(M mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor)` 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.`fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor)` Trains model based on the specified data.
    `protected <K,V> DecisionTreeNode` | DecisionTree.`updateModel(DecisionTreeNode mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor)` 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<IgniteModel<Vector,Double>>` | GDBOnTreesLearningStrategy.`update(GDBTrainer.GDBModel mdlToUpdate, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> vectorizer)` 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.`fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor)` Trains model based on the specified data.
    `protected <K,V> ModelsComposition` | RandomForestTrainer.`updateModel(ModelsComposition mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor)` Gets state of model in arguments, update in according to new data and return new model.

  - ### Uses of DatasetBuilder in org.apache.ignite.ml.util.generators

    Methods in org.apache.ignite.ml.util.generators that return DatasetBuilder
    Modifier and Type | Method and Description
    --- | ---
    `default DatasetBuilder<Vector,Double>` | DataStreamGenerator.`asDatasetBuilder(int datasetSize, IgniteBiPredicate<Vector,Double> filter, int partitions)` Convert first N values from stream to `DatasetBuilder`.
    `default DatasetBuilder<Vector,Double>` | DataStreamGenerator.`asDatasetBuilder(int datasetSize, IgniteBiPredicate<Vector,Double> filter, int partitions, UpstreamTransformerBuilder upstreamTransformerBuilder)` Convert first N values from stream to `DatasetBuilder`.
    `default DatasetBuilder<Vector,Double>` | DataStreamGenerator.`asDatasetBuilder(int datasetSize, int partitions)` Convert first N values from stream to `DatasetBuilder`.

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