## Uses of Interface
org.apache.ignite.ml.preprocessing.Preprocessor

- Packages that use Preprocessor
  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.feature.extractor | Package for upstream object vectorizations.
  org.apache.ignite.ml.dataset.feature.extractor.impl | Package contains default implementations of `Vectorizer`.
  org.apache.ignite.ml.dataset.impl.bootstrapping | Base package for bootstrapped implementation of machine learning dataset.
  org.apache.ignite.ml.dataset.primitive | Package that contains basic primitives build on top of `Dataset`.
  org.apache.ignite.ml.dataset.primitive.builder.data | Contains partition `data` builders.
  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.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.developer | Contains Developer API preprocessors.
  org.apache.ignite.ml.preprocessing.encoding | Contains encoding preprocessors.
  org.apache.ignite.ml.preprocessing.encoding.frequency | Contains frequency encoding preprocessor.
  org.apache.ignite.ml.preprocessing.encoding.label | Contains label encoding preprocessor.
  org.apache.ignite.ml.preprocessing.encoding.onehotencoder | Contains one hot encoding preprocessor.
  org.apache.ignite.ml.preprocessing.encoding.stringencoder | Contains string encoding preprocessor.
  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.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.cursor | Util classes used for score calculation.
  org.apache.ignite.ml.selection.scoring.evaluator | Package for model evaluator classes.
  org.apache.ignite.ml.structures.partition | Contains internal APIs for dataset partitioned labeled datasets.
  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.data | Contains data and data builder required for decision tree trainers built on top of partition based dataset.
  org.apache.ignite.ml.tree.randomforest | Contains random forest implementation classes.

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

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

    Methods in org.apache.ignite.ml.clustering.kmeans with parameters of type Preprocessor
    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 Preprocessor in org.apache.ignite.ml.composition.bagging

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

    Methods in org.apache.ignite.ml.composition.boosting with parameters of type Preprocessor
    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` | GDBRegressionTrainer.`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` | 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).
    `<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 Preprocessor in org.apache.ignite.ml.composition.boosting.convergence

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

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

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

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

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

    Constructors in org.apache.ignite.ml.composition.boosting.convergence.simple with parameters of type Preprocessor
    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 Preprocessor in org.apache.ignite.ml.composition.combinators.parallel

    Methods in org.apache.ignite.ml.composition.combinators.parallel with parameters of type Preprocessor
    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 Preprocessor in org.apache.ignite.ml.composition.combinators.sequential

    Methods in org.apache.ignite.ml.composition.combinators.sequential with parameters of type Preprocessor
    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 Preprocessor in org.apache.ignite.ml.composition.stacking

    Methods in org.apache.ignite.ml.composition.stacking with parameters of type Preprocessor
    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 Preprocessor in org.apache.ignite.ml.dataset

    Methods in org.apache.ignite.ml.dataset with parameters of type Preprocessor
    Modifier and Type | Method and Description
    --- | ---
    `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> SimpleDataset<C>` | DatasetFactory.`createSimpleDataset(Ignite ignite, IgniteCache<K,V> upstreamCache, 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(Ignite ignite, IgniteCache<K,V> upstreamCache, LearningEnvironmentBuilder envBuilder, Preprocessor<K,V> featureExtractor)` Creates a new instance of distributed `SimpleDataset` using the specified `featureExtractor`.
    `static <K,V,CO extends Serializable> SimpleDataset<EmptyContext>` | DatasetFactory.`createSimpleDataset(Ignite ignite, IgniteCache<K,V> upstreamCache, Preprocessor<K,V> featureExtractor)` Creates a new instance of distributed `SimpleDataset` using the specified `featureExtractor`.
    `static <K,V,C extends Serializable,CO extends Serializable> SimpleDataset<C>` | DatasetFactory.`createSimpleDataset(Map<K,V> upstreamMap, int partitions, LearningEnvironmentBuilder envBuilder, PartitionContextBuilder<K,V,C> partCtxBuilder, Preprocessor<K,V> featureExtractor)` Creates a new instance of local `SimpleDataset` using the specified `partCtxBuilder` and `featureExtractor`.
    `static <K,V,CO extends Serializable> SimpleDataset<EmptyContext>` | DatasetFactory.`createSimpleDataset(Map<K,V> upstreamMap, int partitions, LearningEnvironmentBuilder envBuilder, Preprocessor<K,V> featureExtractor)` Creates a new instance of local `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`.
    `static <K,V,C extends Serializable,CO extends Serializable> SimpleLabeledDataset<C>` | DatasetFactory.`createSimpleLabeledDataset(Ignite ignite, IgniteCache<K,V> upstreamCache, 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(Ignite ignite, LearningEnvironmentBuilder envBuilder, IgniteCache<K,V> upstreamCache, Preprocessor<K,V> vectorizer)` Creates a new instance of distributed `SimpleLabeledDataset` using the specified `featureExtractor` and `lbExtractor`.
    `static <K,V,C extends Serializable,CO extends Serializable> SimpleLabeledDataset<C>` | DatasetFactory.`createSimpleLabeledDataset(Map<K,V> upstreamMap, int partitions, LearningEnvironmentBuilder envBuilder, PartitionContextBuilder<K,V,C> partCtxBuilder, Preprocessor<K,V> vectorizer)` Creates a new instance of local `SimpleLabeledDataset` using the specified `partCtxBuilder`, `featureExtractor` and `lbExtractor`.
    `static <K,V,CO extends Serializable> SimpleLabeledDataset<EmptyContext>` | DatasetFactory.`createSimpleLabeledDataset(Map<K,V> upstreamMap, LearningEnvironmentBuilder envBuilder, int partitions, Preprocessor<K,V> vectorizer)` Creates a new instance of local `SimpleLabeledDataset` using the specified `featureExtractor` and `lbExtractor`.

  - ### Uses of Preprocessor in org.apache.ignite.ml.dataset.feature.extractor

    Classes in org.apache.ignite.ml.dataset.feature.extractor that implement Preprocessor
    Modifier and Type | Class and Description
    --- | ---
    `static class` | `ExtractionUtils.ArrayLikeObjectLabelVectorizer<K,V>` Vectorizer extracting vectors from array-like structure with finite size and integer coordinates.
    `static class` | `ExtractionUtils.ArrayLikeVectorizer<K,V>` Vectorizer extracting vectors from array-like structure with finite size and integer coordinates.
    `static class` | `ExtractionUtils.DefaultLabelVectorizer<K,V,C extends Serializable>` Vectorizer with double-label containing on same level as feature values.
    `static class` | `ExtractionUtils.IntCoordObjectLabelVectorizer<K,V>` Vectorizer with integer coordinates.
    `static class` | `ExtractionUtils.IntCoordVectorizer<K,V>` Vectorizer with integer coordinates.
    `static class` | `ExtractionUtils.ObjectLabelVectorizer<K,V,C extends Serializable>` Vectorizer with String-label containing on same level as feature values.
    `static class` | `ExtractionUtils.StringCoordVectorizer<K,V>` Vectorizer with String-coordinates.
    `class` | `Vectorizer<K,V,C extends Serializable,L>` Class for extracting labeled vectors from upstream.
    `static class` | `Vectorizer.VectorizerAdapter<K,V,C extends Serializable,L>` Utility class for convenient overridings.

  - ### Uses of Preprocessor in org.apache.ignite.ml.dataset.feature.extractor.impl

    Classes in org.apache.ignite.ml.dataset.feature.extractor.impl that implement Preprocessor
    Modifier and Type | Class and Description
    --- | ---
    `class` | `BinaryObjectVectorizer<K>` Vectorizer on binary objects.
    `class` | `DoubleArrayVectorizer<K>` Vectorizer on arrays of doubles.
    `class` | `DummyVectorizer<K>` Vectorizer on Vector.
    `class` | `LabeledDummyVectorizer<K,L>` Vectorizer on LabeledVector.
    `class` | `ObjectArrayVectorizer<K>` Vectorizer on arrays of objects.

  - ### Uses of Preprocessor in org.apache.ignite.ml.dataset.impl.bootstrapping

    Constructors in org.apache.ignite.ml.dataset.impl.bootstrapping with parameters of type Preprocessor
    Constructor and Description
    `BootstrappedDatasetBuilder(Preprocessor<K,V> preprocessor, int samplesCnt, double subsampleSize)` Creates an instance of BootstrappedDatasetBuilder.

  - ### Uses of Preprocessor in org.apache.ignite.ml.dataset.primitive

    Constructors in org.apache.ignite.ml.dataset.primitive with parameters of type Preprocessor
    Constructor and Description
    `FeatureMatrixWithLabelsOnHeapDataBuilder(Preprocessor<K,V> preprocessor)` Constructs a new instance of decision tree data builder.

  - ### Uses of Preprocessor in org.apache.ignite.ml.dataset.primitive.builder.data

    Constructors in org.apache.ignite.ml.dataset.primitive.builder.data with parameters of type Preprocessor
    Constructor and Description
    `SimpleDatasetDataBuilder(Preprocessor<K,V> preprocessor)` Construct a new instance of partition `data` builder that makes `SimpleDatasetData`.
    `SimpleLabeledDatasetDataBuilder(Preprocessor<K,V> vectorizer)` Constructs a new instance of partition `data` builder that makes `SimpleLabeledDatasetData`.

  - ### Uses of Preprocessor in org.apache.ignite.ml.knn

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

    Constructors in org.apache.ignite.ml.knn with parameters of type Preprocessor
    Constructor and Description
    `KNNPartitionDataBuilder(Preprocessor<K,V> preprocessor, SpatialIndexType spatialIdxType, DistanceMeasure distanceMeasure)` Constructs a new instance of KNN partition data builder.

  - ### Uses of Preprocessor in org.apache.ignite.ml.knn.ann

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

    Methods in org.apache.ignite.ml.knn.utils with parameters of type Preprocessor
    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 Preprocessor in org.apache.ignite.ml.multiclass

    Methods in org.apache.ignite.ml.multiclass with parameters of type Preprocessor
    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 Preprocessor in org.apache.ignite.ml.naivebayes.compound

    Methods in org.apache.ignite.ml.naivebayes.compound with parameters of type Preprocessor
    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 Preprocessor in org.apache.ignite.ml.naivebayes.discrete

    Methods in org.apache.ignite.ml.naivebayes.discrete with parameters of type Preprocessor
    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 Preprocessor in org.apache.ignite.ml.naivebayes.gaussian

    Methods in org.apache.ignite.ml.naivebayes.gaussian with parameters of type Preprocessor
    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 Preprocessor in org.apache.ignite.ml.nn

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

    Methods in org.apache.ignite.ml.pipeline that return Preprocessor
    Modifier and Type | Method and Description
    --- | ---
    `Preprocessor<K,V>` | Pipeline.`getFinalPreprocessor()` Returns the final preprocessor for evaluation needs.
    `Preprocessor<K,V>` | PipelineMdl.`getPreprocessor()`

    Methods in org.apache.ignite.ml.pipeline with parameters of type Preprocessor
    Modifier and Type | Method and Description
    --- | ---
    `PipelineMdl<K,V>` | PipelineMdl.`withPreprocessor(Preprocessor<K,V> preprocessor)`

  - ### Uses of Preprocessor in org.apache.ignite.ml.preprocessing

    Methods in org.apache.ignite.ml.preprocessing that return Preprocessor
    Modifier and Type | Method and Description
    --- | ---
    `default Preprocessor<K,V>` | PreprocessingTrainer.`fit(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> basePreprocessor)` Fits preprocessor.
    `default Preprocessor<K,V>` | PreprocessingTrainer.`fit(Ignite ignite, IgniteCache<K,V> cache, Preprocessor<K,V> basePreprocessor)` Fits preprocessor.
    `Preprocessor<K,V>` | PreprocessingTrainer.`fit(LearningEnvironmentBuilder envBuilder, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> basePreprocessor)` Fits preprocessor.
    `default Preprocessor<K,V>` | PreprocessingTrainer.`fit(LearningEnvironmentBuilder envBuilder, Ignite ignite, IgniteCache<K,V> cache, Preprocessor<K,V> basePreprocessor)` Fits preprocessor.
    `default Preprocessor<K,V>` | PreprocessingTrainer.`fit(LearningEnvironmentBuilder envBuilder, Map<K,V> data, int parts, Preprocessor<K,V> basePreprocessor)` Fits preprocessor.
    `default Preprocessor<K,V>` | PreprocessingTrainer.`fit(Map<K,V> data, int parts, Preprocessor<K,V> basePreprocessor)` Fits preprocessor.
    `default <L1,L2> Preprocessor<K,V>` | Preprocessor.`map(IgniteFunction<LabeledVector<L1>,LabeledVector<L2>> func)` Map vectorizer answer.

    Methods in org.apache.ignite.ml.preprocessing with parameters of type Preprocessor
    Modifier and Type | Method and Description
    --- | ---
    `default Preprocessor<K,V>` | PreprocessingTrainer.`fit(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> basePreprocessor)` Fits preprocessor.
    `default Preprocessor<K,V>` | PreprocessingTrainer.`fit(Ignite ignite, IgniteCache<K,V> cache, Preprocessor<K,V> basePreprocessor)` Fits preprocessor.
    `Preprocessor<K,V>` | PreprocessingTrainer.`fit(LearningEnvironmentBuilder envBuilder, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> basePreprocessor)` Fits preprocessor.
    `default Preprocessor<K,V>` | PreprocessingTrainer.`fit(LearningEnvironmentBuilder envBuilder, Ignite ignite, IgniteCache<K,V> cache, Preprocessor<K,V> basePreprocessor)` Fits preprocessor.
    `default Preprocessor<K,V>` | PreprocessingTrainer.`fit(LearningEnvironmentBuilder envBuilder, Map<K,V> data, int parts, Preprocessor<K,V> basePreprocessor)` Fits preprocessor.
    `default Preprocessor<K,V>` | PreprocessingTrainer.`fit(Map<K,V> data, int parts, Preprocessor<K,V> basePreprocessor)` Fits preprocessor.
    `default LearningEnvironment` | PreprocessingTrainer.`learningEnvironment(Preprocessor<K,V> basePreprocessor)` Returns local learning environment with initialized deploying context by base preprocessor.

  - ### Uses of Preprocessor in org.apache.ignite.ml.preprocessing.binarization

    Classes in org.apache.ignite.ml.preprocessing.binarization that implement Preprocessor
    Modifier and Type | Class and Description
    --- | ---
    `class` | `BinarizationPreprocessor<K,V>` Preprocessing function that makes binarization.

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

    Constructors in org.apache.ignite.ml.preprocessing.binarization with parameters of type Preprocessor
    Constructor and Description
    `BinarizationPreprocessor(double threshold, Preprocessor<K,V> basePreprocessor)` Constructs a new instance of Binarization preprocessor.

  - ### Uses of Preprocessor in org.apache.ignite.ml.preprocessing.developer

    Classes in org.apache.ignite.ml.preprocessing.developer that implement Preprocessor
    Modifier and Type | Class and Description
    --- | ---
    `class` | `MappedPreprocessor<K,V,L0,L1>` Mapped Preprocessor.
    `class` | `PatchedPreprocessor<K,V,L1,L2>` Preprocessing function that makes binarization.

    Constructors in org.apache.ignite.ml.preprocessing.developer with parameters of type Preprocessor
    Constructor and Description
    `MappedPreprocessor(Preprocessor<K,V> original, IgniteFunction<LabeledVector<L0>,LabeledVector<L1>> andThen)` Creates an instance of MappedPreprocessor.
    `PatchedPreprocessor(IgniteFunction<LabeledVector<L1>,LabeledVector<L2>> lbPatcher, Preprocessor<K,V> basePreprocessor)` Constructs a new instance of Binarization preprocessor.

  - ### Uses of Preprocessor in org.apache.ignite.ml.preprocessing.encoding

    Classes in org.apache.ignite.ml.preprocessing.encoding that implement Preprocessor
    Modifier and Type | Class and Description
    --- | ---
    `class` | `EncoderPreprocessor<K,V>` Preprocessing function that makes encoding.

    Fields in org.apache.ignite.ml.preprocessing.encoding declared as Preprocessor
    Modifier and Type | Field and Description
    --- | ---
    `protected Preprocessor<K,V>` | EncoderPreprocessor.`basePreprocessor` Base preprocessor.

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

    Constructors in org.apache.ignite.ml.preprocessing.encoding with parameters of type Preprocessor
    Constructor and Description
    `EncoderPreprocessor(Map<String,Integer>[] encodingValues, Preprocessor<K,V> basePreprocessor, Set<Integer> handledIndices)` Constructs a new instance of Encoder preprocessor.
    `EncoderPreprocessor(Map<String,Integer> labelFrequencies, Preprocessor<K,V> basePreprocessor)` Constructs a new instance of Encoder preprocessor.

  - ### Uses of Preprocessor in org.apache.ignite.ml.preprocessing.encoding.frequency

    Classes in org.apache.ignite.ml.preprocessing.encoding.frequency that implement Preprocessor
    Modifier and Type | Class and Description
    --- | ---
    `class` | `FrequencyEncoderPreprocessor<K,V>` Preprocessing function that makes Frequency encoding.

    Constructors in org.apache.ignite.ml.preprocessing.encoding.frequency with parameters of type Preprocessor
    Constructor and Description
    `FrequencyEncoderPreprocessor(Map<String,Double>[] encodingFrequencies, Preprocessor<K,V> basePreprocessor, Set<Integer> handledIndices)` Constructs a new instance of Frequency Encoder preprocessor.

  - ### Uses of Preprocessor in org.apache.ignite.ml.preprocessing.encoding.label

    Classes in org.apache.ignite.ml.preprocessing.encoding.label that implement Preprocessor
    Modifier and Type | Class and Description
    --- | ---
    `class` | `LabelEncoderPreprocessor<K,V>` Preprocessing function that makes Label encoding.

    Constructors in org.apache.ignite.ml.preprocessing.encoding.label with parameters of type Preprocessor
    Constructor and Description
    `LabelEncoderPreprocessor(Map<String,Integer> labelFrequencies, Preprocessor<K,V> basePreprocessor)` Constructs a new instance of Label Encoder preprocessor.

  - ### Uses of Preprocessor in org.apache.ignite.ml.preprocessing.encoding.onehotencoder

    Classes in org.apache.ignite.ml.preprocessing.encoding.onehotencoder that implement Preprocessor
    Modifier and Type | Class and Description
    --- | ---
    `class` | `OneHotEncoderPreprocessor<K,V>` Preprocessing function that makes one-hot encoding.

    Constructors in org.apache.ignite.ml.preprocessing.encoding.onehotencoder with parameters of type Preprocessor
    Constructor and Description
    `OneHotEncoderPreprocessor(Map<String,Integer>[] encodingValues, Preprocessor<K,V> basePreprocessor, Set<Integer> handledIndices)` Constructs a new instance of One Hot Encoder preprocessor.

  - ### Uses of Preprocessor in org.apache.ignite.ml.preprocessing.encoding.stringencoder

    Classes in org.apache.ignite.ml.preprocessing.encoding.stringencoder that implement Preprocessor
    Modifier and Type | Class and Description
    --- | ---
    `class` | `StringEncoderPreprocessor<K,V>` Preprocessing function that makes String encoding.

    Constructors in org.apache.ignite.ml.preprocessing.encoding.stringencoder with parameters of type Preprocessor
    Constructor and Description
    `StringEncoderPreprocessor(Map<String,Integer>[] encodingValues, Preprocessor<K,V> basePreprocessor, Set<Integer> handledIndices)` Constructs a new instance of String Encoder preprocessor.

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

    Classes in org.apache.ignite.ml.preprocessing.finalization that implement Preprocessor
    Modifier and Type | Class and Description
    --- | ---
    `class` | `VectorFinalizationPreprocessor<K,V>` Preprocessing function that makes vector finalization.

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

    Constructors in org.apache.ignite.ml.preprocessing.finalization with parameters of type Preprocessor
    Constructor and Description
    `VectorFinalizationPreprocessor(Preprocessor<K,V> basePreprocessor)` Constructs a new instance of Binarization preprocessor.

  - ### Uses of Preprocessor in org.apache.ignite.ml.preprocessing.imputing

    Classes in org.apache.ignite.ml.preprocessing.imputing that implement Preprocessor
    Modifier and Type | Class and Description
    --- | ---
    `class` | `ImputerPreprocessor<K,V>` Preprocessing function that makes imputing.

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

    Constructors in org.apache.ignite.ml.preprocessing.imputing with parameters of type Preprocessor
    Constructor and Description
    `ImputerPreprocessor(Vector imputingValues, Preprocessor<K,V> basePreprocessor)` Constructs a new instance of imputing preprocessor.

  - ### Uses of Preprocessor in org.apache.ignite.ml.preprocessing.maxabsscaling

    Classes in org.apache.ignite.ml.preprocessing.maxabsscaling that implement Preprocessor
    Modifier and Type | Class and Description
    --- | ---
    `class` | `MaxAbsScalerPreprocessor<K,V>` The preprocessing function that makes maxabsscaling, transforms features to the scale `[-1,+1]`.

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

    Constructors in org.apache.ignite.ml.preprocessing.maxabsscaling with parameters of type Preprocessor
    Constructor and Description
    `MaxAbsScalerPreprocessor(double[] maxAbs, Preprocessor<K,V> basePreprocessor)` Constructs a new instance of maxabsscaling preprocessor.

  - ### Uses of Preprocessor in org.apache.ignite.ml.preprocessing.minmaxscaling

    Classes in org.apache.ignite.ml.preprocessing.minmaxscaling that implement Preprocessor
    Modifier and Type | Class and Description
    --- | ---
    `class` | `MinMaxScalerPreprocessor<K,V>` Preprocessing function that makes minmaxscaling.

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

    Constructors in org.apache.ignite.ml.preprocessing.minmaxscaling with parameters of type Preprocessor
    Constructor and Description
    `MinMaxScalerPreprocessor(double[] min, double[] max, Preprocessor<K,V> basePreprocessor)` Constructs a new instance of minmaxscaling preprocessor.

  - ### Uses of Preprocessor in org.apache.ignite.ml.preprocessing.normalization

    Classes in org.apache.ignite.ml.preprocessing.normalization that implement Preprocessor
    Modifier and Type | Class and Description
    --- | ---
    `class` | `NormalizationPreprocessor<K,V>` Preprocessing function that makes normalization.

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

    Constructors in org.apache.ignite.ml.preprocessing.normalization with parameters of type Preprocessor
    Constructor and Description
    `NormalizationPreprocessor(int p, Preprocessor<K,V> basePreprocessor)` Constructs a new instance of Normalization preprocessor.

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

    Classes in org.apache.ignite.ml.preprocessing.standardscaling that implement Preprocessor
    Modifier and Type | Class and Description
    --- | ---
    `class` | `StandardScalerPreprocessor<K,V>` The preprocessing function that makes standard scaling, transforms features to make `mean` equal to `0` and `variance` equal to `1`.

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

    Constructors in org.apache.ignite.ml.preprocessing.standardscaling with parameters of type Preprocessor
    Constructor and Description
    `StandardScalerPreprocessor(double[] means, double[] sigmas, Preprocessor<K,V> basePreprocessor)` Constructs a new instance of standardscaling preprocessor.

  - ### Uses of Preprocessor in org.apache.ignite.ml.regressions.linear

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

    Methods in org.apache.ignite.ml.regressions.logistic with parameters of type Preprocessor
    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 Preprocessor in org.apache.ignite.ml.selection.cv

    Fields in org.apache.ignite.ml.selection.cv declared as Preprocessor
    Modifier and Type | Field and Description
    --- | ---
    `protected Preprocessor<K,V>` | AbstractCrossValidation.`preprocessor` Preprocessor.

    Methods in org.apache.ignite.ml.selection.cv with parameters of type Preprocessor
    Modifier and Type | Method and Description
    --- | ---
    `AbstractCrossValidation<M,K,V>` | AbstractCrossValidation.`withPreprocessor(Preprocessor<K,V> preprocessor)`

  - ### Uses of Preprocessor in org.apache.ignite.ml.selection.scoring.cursor

    Constructors in org.apache.ignite.ml.selection.scoring.cursor with parameters of type Preprocessor
    Constructor and Description
    `CacheBasedLabelPairCursor(IgniteCache<K,V> upstreamCache, IgniteBiPredicate<K,V> filter, Preprocessor<K,V> preprocessor, IgniteModel<Vector,L> mdl)` Constructs a new instance of cache based truth with prediction cursor.
    `CacheBasedLabelPairCursor(IgniteCache<K,V> upstreamCache, Preprocessor<K,V> preprocessor, IgniteModel<Vector,L> mdl)` Constructs a new instance of cache based truth with prediction cursor.
    `LocalLabelPairCursor(Map<K,V> upstreamMap, IgniteBiPredicate<K,V> filter, Preprocessor<K,V> preprocessor, IgniteModel<Vector,L> mdl)` Constructs a new instance of local truth with prediction cursor.

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

    Methods in org.apache.ignite.ml.selection.scoring.evaluator with parameters of type Preprocessor
    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.
    `static <K,V> double` | Evaluator.`evaluate(IgniteCache<K,V> dataCache, IgniteBiPredicate<K,V> filter, IgniteModel<Vector,Double> mdl, Preprocessor<K,V> preprocessor, Metric metric)` Computes the given metric on the given cache.
    `static <K,V> double` | Evaluator.`evaluate(IgniteCache<K,V> dataCache, IgniteBiPredicate<K,V> filter, IgniteModel<Vector,Double> mdl, Preprocessor<K,V> preprocessor, MetricName metric)` Computes the given metric on the given cache.
    `static <K,V> double` | Evaluator.`evaluate(IgniteCache<K,V> dataCache, IgniteModel<Vector,Double> mdl, Preprocessor<K,V> preprocessor, Metric metric)` Computes the given metric on the given cache.
    `static <K,V> double` | Evaluator.`evaluate(IgniteCache<K,V> dataCache, IgniteModel<Vector,Double> mdl, Preprocessor<K,V> preprocessor, MetricName metric)` Computes the given metric on the given cache.
    `static <K,V> double` | Evaluator.`evaluate(Map<K,V> dataCache, IgniteBiPredicate<K,V> filter, IgniteModel<Vector,Double> mdl, Preprocessor<K,V> preprocessor, Metric metric)` Computes the given metric on the given cache.
    `static <K,V> double` | Evaluator.`evaluate(Map<K,V> dataCache, IgniteBiPredicate<K,V> filter, IgniteModel<Vector,Double> mdl, Preprocessor<K,V> preprocessor, MetricName metric)` Computes the given metric on the given cache.
    `static <K,V> double` | Evaluator.`evaluate(Map<K,V> dataCache, IgniteModel<Vector,Double> mdl, Preprocessor<K,V> preprocessor, Metric metric)` Computes the given metric on the given cache.
    `static <K,V> double` | Evaluator.`evaluate(Map<K,V> dataCache, IgniteModel<Vector,Double> mdl, Preprocessor<K,V> preprocessor, MetricName metric)` Computes the given metric on the given cache.
    `static <K,V> EvaluationResult` | Evaluator.`evaluateBinaryClassification(IgniteCache<K,V> dataCache, IgniteBiPredicate<K,V> filter, IgniteModel<Vector,Double> mdl, Preprocessor<K,V> preprocessor)` Evaluate binary classifier by default metrics (see package classification).
    `static <K,V> EvaluationResult` | Evaluator.`evaluateBinaryClassification(IgniteCache<K,V> dataCache, IgniteModel<Vector,Double> mdl, Preprocessor<K,V> preprocessor)` Evaluate binary classifier by default metrics (see package classification).
    `static <K,V> EvaluationResult` | Evaluator.`evaluateBinaryClassification(Map<K,V> dataCache, IgniteBiPredicate<K,V> filter, IgniteModel<Vector,Double> mdl, Preprocessor<K,V> preprocessor)` Evaluate binary classifier by default metrics (see package classification).
    `static <K,V> EvaluationResult` | Evaluator.`evaluateBinaryClassification(Map<K,V> dataCache, IgniteModel<Vector,Double> mdl, Preprocessor<K,V> preprocessor)` Evaluate binary classifier by default metrics (see package classification).
    `static <K,V> EvaluationResult` | Evaluator.`evaluateRegression(IgniteCache<K,V> dataCache, IgniteBiPredicate<K,V> filter, IgniteModel<Vector,Double> mdl, Preprocessor<K,V> preprocessor)` Evaluate regression by default metrics (see package regression).
    `static <K,V> EvaluationResult` | Evaluator.`evaluateRegression(IgniteCache<K,V> dataCache, IgniteModel<Vector,Double> mdl, Preprocessor<K,V> preprocessor)` Evaluate regression by default metrics (see package regression).
    `static <K,V> EvaluationResult` | Evaluator.`evaluateRegression(Map<K,V> dataCache, IgniteBiPredicate<K,V> filter, IgniteModel<Vector,Double> mdl, Preprocessor<K,V> preprocessor)` Evaluate regression by default metrics (see package regression).
    `static <K,V> EvaluationResult` | Evaluator.`evaluateRegression(Map<K,V> dataCache, IgniteModel<Vector,Double> mdl, Preprocessor<K,V> preprocessor)` Evaluate regression by default metrics (see package regression).

  - ### Uses of Preprocessor in org.apache.ignite.ml.structures.partition

    Constructors in org.apache.ignite.ml.structures.partition with parameters of type Preprocessor
    Constructor and Description
    `LabeledDatasetPartitionDataBuilderOnHeap(Preprocessor<K,V> preprocessor)` Constructs a new instance of SVM partition data builder.
    `LabelPartitionDataBuilderOnHeap(Preprocessor<K,V> preprocessor)` Constructs a new instance of Label partition data builder.

  - ### Uses of Preprocessor in org.apache.ignite.ml.svm

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

    Subinterfaces of Preprocessor in org.apache.ignite.ml.trainers
    Modifier and Type | Interface and Description
    --- | ---
    `interface` | `FeatureLabelExtractor<K,V,L>` Class fro extracting features and vectors from upstream.

    Methods in org.apache.ignite.ml.trainers with parameters of type Preprocessor
    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> M` | DatasetTrainer.`fit(Ignite ignite, IgniteCache<K,V> cache, IgniteBiPredicate<K,V> filter, Preprocessor<K,V> preprocessor)` Trains model based on the specified data.
    `<K,V> M` | DatasetTrainer.`fit(Ignite ignite, IgniteCache<K,V> cache, Preprocessor<K,V> preprocessor)` Trains model based on the specified data.
    `<K,V> M` | DatasetTrainer.`fit(Map<K,V> data, IgniteBiPredicate<K,V> filter, int parts, Preprocessor<K,V> preprocessor)` Trains model based on the specified data.
    `<K,V> M` | DatasetTrainer.`fit(Map<K,V> data, int parts, Preprocessor<K,V> preprocessor)` 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> AdaptableDatasetModel<I,O,IW,OW,M>` | AdaptableDatasetTrainer.`fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> extractor)` 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.
    `<K,V> M` | DatasetTrainer.`update(M mdl, Ignite ignite, IgniteCache<K,V> cache, IgniteBiPredicate<K,V> filter, Preprocessor<K,V> preprocessor)` Gets state of model in arguments, update in according to new data and return new model.
    `<K,V> M` | DatasetTrainer.`update(M mdl, Ignite ignite, IgniteCache<K,V> cache, Preprocessor<K,V> preprocessor)` Gets state of model in arguments, update in according to new data and return new model.
    `<K,V> M` | DatasetTrainer.`update(M mdl, Map<K,V> data, IgniteBiPredicate<K,V> filter, int parts, Preprocessor<K,V> preprocessor)` Gets state of model in arguments, update in according to new data and return new model.
    `<K,V> M` | DatasetTrainer.`update(M mdl, Map<K,V> data, int parts, Preprocessor<K,V> preprocessor)` Gets state of model in arguments, update 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 Preprocessor in org.apache.ignite.ml.tree

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

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

    Constructors in org.apache.ignite.ml.tree.data with parameters of type Preprocessor
    Constructor and Description
    `DecisionTreeDataBuilder(Preprocessor<K,V> preprocessor, boolean buildIdx)` Constructs a new instance of decision tree data builder.

  - ### Uses of Preprocessor in org.apache.ignite.ml.tree.randomforest

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

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