## Uses of Class
org.apache.ignite.ml.trainers.DatasetTrainer

- Packages that use DatasetTrainer
  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 | Contains classes for ensemble of models implementation.
  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.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.knn | Contains main APIs for kNN algorithms.
  org.apache.ignite.ml.knn.ann | Contains main APIs for ANN classification algorithms.
  org.apache.ignite.ml.knn.classification | Contains main APIs for kNN classification algorithms.
  org.apache.ignite.ml.knn.regression | Contains helper classes for kNN regression algorithms.
  org.apache.ignite.ml.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.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.svm | Contains main APIs for SVM(support vector machines) algorithms.
  org.apache.ignite.ml.trainers | Contains model trainers.
  org.apache.ignite.ml.tree | Root package for decision trees.
  org.apache.ignite.ml.tree.boosting | Contains implementation of gradient boosting on trees.
  org.apache.ignite.ml.tree.randomforest | Contains random forest implementation classes.

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

    Subclasses of DatasetTrainer in org.apache.ignite.ml.clustering.gmm
    Modifier and Type | Class and Description
    --- | ---
    `class` | `GmmTrainer` Traner for GMM model.

  - ### Uses of DatasetTrainer in org.apache.ignite.ml.clustering.kmeans

    Subclasses of DatasetTrainer in org.apache.ignite.ml.clustering.kmeans
    Modifier and Type | Class and Description
    --- | ---
    `class` | `KMeansTrainer` The trainer for KMeans algorithm.

  - ### Uses of DatasetTrainer in org.apache.ignite.ml.composition

    Methods in org.apache.ignite.ml.composition that return DatasetTrainer
    Modifier and Type | Method and Description
    --- | ---
    `static <I,O,M extends IgniteModel<I,O>,L> DatasetTrainer<IgniteModel<I,O>,L>` | CompositionUtils.`unsafeCoerce(DatasetTrainer<? extends M,L> trainer)` Perform blurring of model type of given trainer to `IgniteModel<I, O>`, where I, O are input and output types of original model.

    Methods in org.apache.ignite.ml.composition with parameters of type DatasetTrainer
    Modifier and Type | Method and Description
    --- | ---
    `static <I,O,M extends IgniteModel<I,O>,L> DatasetTrainer<IgniteModel<I,O>,L>` | CompositionUtils.`unsafeCoerce(DatasetTrainer<? extends M,L> trainer)` Perform blurring of model type of given trainer to `IgniteModel<I, O>`, where I, O are input and output types of original model.

  - ### Uses of DatasetTrainer in org.apache.ignite.ml.composition.bagging

    Subclasses of DatasetTrainer in org.apache.ignite.ml.composition.bagging
    Modifier and Type | Class and Description
    --- | ---
    `class` | `BaggedTrainer<L>` Trainer encapsulating logic of bootstrap aggregating (bagging).

    Constructors in org.apache.ignite.ml.composition.bagging with parameters of type DatasetTrainer
    Constructor and Description
    `BaggedTrainer(DatasetTrainer<? extends IgniteModel,L> tr, PredictionsAggregator aggregator, int ensembleSize, double subsampleRatio, int featuresVectorSize, int featureSubspaceDim)` Construct instance of this class with given parameters.

  - ### Uses of DatasetTrainer in org.apache.ignite.ml.composition.boosting

    Subclasses of DatasetTrainer in org.apache.ignite.ml.composition.boosting
    Modifier and Type | Class and Description
    --- | ---
    `class` | `GDBBinaryClassifierTrainer` Trainer for binary classifier using Gradient Boosting.
    `class` | `GDBRegressionTrainer` Trainer for regressor using Gradient Boosting.
    `class` | `GDBTrainer` Abstract Gradient Boosting trainer.

    Fields in org.apache.ignite.ml.composition.boosting with type parameters of type DatasetTrainer
    Modifier and Type | Field and Description
    --- | ---
    `protected IgniteSupplier<DatasetTrainer<? extends IgniteModel<Vector,Double>,Double>>` | GDBLearningStrategy.`baseMdlTrainerBuilder` Base model trainer builder.

    Methods in org.apache.ignite.ml.composition.boosting that return DatasetTrainer
    Modifier and Type | Method and Description
    --- | ---
    `protected abstract @NotNull DatasetTrainer<? extends IgniteModel<Vector,Double>,Double>` | GDBTrainer.`buildBaseModelTrainer()` Returns regressor model trainer for one step of GDB.

    Method parameters in org.apache.ignite.ml.composition.boosting with type arguments of type DatasetTrainer
    Modifier and Type | Method and Description
    --- | ---
    `GDBLearningStrategy` | GDBLearningStrategy.`withBaseModelTrainerBuilder(IgniteSupplier<DatasetTrainer<? extends IgniteModel<Vector,Double>,Double>> buildBaseMdlTrainer)` Sets base model builder.

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

    Subclasses of DatasetTrainer in org.apache.ignite.ml.composition.combinators.parallel
    Modifier and Type | Class and Description
    --- | ---
    `class` | `TrainersParallelComposition<I,O,L>` This class represents a parallel composition of trainers.

    Methods in org.apache.ignite.ml.composition.combinators.parallel with type parameters of type DatasetTrainer
    Modifier and Type | Method and Description
    --- | ---
    `static <I,O,M extends IgniteModel<I,O>,T extends DatasetTrainer<M,L>,L> TrainersParallelComposition<I,O,L>` | TrainersParallelComposition.`of(List<T> trainers)` Create parallel composition of trainers contained in a given list.

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

    Subclasses of DatasetTrainer in org.apache.ignite.ml.composition.combinators.sequential
    Modifier and Type | Class and Description
    --- | ---
    `class` | `TrainersSequentialComposition<I,O1,O2,L>` Sequential composition of trainers.

    Methods in org.apache.ignite.ml.composition.combinators.sequential that return DatasetTrainer
    Modifier and Type | Method and Description
    --- | ---
    `DatasetTrainer<IgniteModel<I,O2>,L>` | TrainersSequentialComposition.`unsafeSimplyTyped()` Performs coersion of this trainer to `DatasetTrainer<IgniteModel<I, O2>, L>`.

    Methods in org.apache.ignite.ml.composition.combinators.sequential with parameters of type DatasetTrainer
    Modifier and Type | Method and Description
    --- | ---
    `static <I,O,L> TrainersSequentialComposition<I,O,O,L>` | TrainersSequentialComposition.`ofSame(DatasetTrainer<? extends IgniteModel<I,O>,L> tr, IgniteBiFunction<Integer,? super IgniteModel<I,O>,IgniteFunction<LabeledVector<L>,LabeledVector<L>>> datasetMapping, IgniteBiPredicate<Integer,IgniteModel<I,O>> shouldStop, IgniteFunction<O,I> out2In)` Construct sequential composition of same trainers.

    Constructors in org.apache.ignite.ml.composition.combinators.sequential with parameters of type DatasetTrainer
    Constructor and Description
    `TrainersSequentialComposition(DatasetTrainer<? extends IgniteModel<I,O1>,L> tr1, DatasetTrainer<? extends IgniteModel<O1,O2>,L> tr2, IgniteBiFunction<Integer,? super IgniteModel<I,O1>,IgniteFunction<LabeledVector<L>,LabeledVector<L>>> datasetMapping)` Create sequential composition of two trainers.
    `TrainersSequentialComposition(DatasetTrainer<? extends IgniteModel<I,O1>,L> tr1, DatasetTrainer<? extends IgniteModel<O1,O2>,L> tr2, IgniteBiFunction<Integer,? super IgniteModel<I,O1>,IgniteFunction<LabeledVector<L>,LabeledVector<L>>> datasetMapping)` Create sequential composition of two trainers.
    `TrainersSequentialComposition(DatasetTrainer<? extends IgniteModel<I,O1>,L> tr1, DatasetTrainer<? extends IgniteModel<O1,O2>,L> tr2, IgniteFunction<? super IgniteModel<I,O1>,IgniteFunction<LabeledVector<L>,LabeledVector<L>>> datasetMapping)` Construct sequential composition of given two trainers.
    `TrainersSequentialComposition(DatasetTrainer<? extends IgniteModel<I,O1>,L> tr1, DatasetTrainer<? extends IgniteModel<O1,O2>,L> tr2, IgniteFunction<? super IgniteModel<I,O1>,IgniteFunction<LabeledVector<L>,LabeledVector<L>>> datasetMapping)` Construct sequential composition of given two trainers.

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

    Subclasses of DatasetTrainer in org.apache.ignite.ml.composition.stacking
    Modifier and Type | Class and Description
    --- | ---
    `class` | `SimpleStackedDatasetTrainer<I,O,AM extends IgniteModel<I,O>,L>` `DatasetTrainer` with same type of input and output of submodels.
    `class` | `StackedDatasetTrainer<IS,IA,O,AM extends IgniteModel<IA,O>,L>` `DatasetTrainer` encapsulating stacking technique for model training.
    `class` | `StackedVectorDatasetTrainer<O,AM extends IgniteModel<Vector,O>,L>` `StackedDatasetTrainer` with `Vector` as submodels input and output.

    Methods in org.apache.ignite.ml.composition.stacking with parameters of type DatasetTrainer
    Modifier and Type | Method and Description
    --- | ---
    `<M1 extends IgniteModel<Matrix,Matrix>> StackedVectorDatasetTrainer<O,AM,L>` | StackedVectorDatasetTrainer.`addMatrix2MatrixTrainer(DatasetTrainer<M1,L> trainer)` Shortcut for adding trainer `Matrix -> Matrix` where this trainer is treated as `Vector -> Vector`, where input `Vector` is turned into `1 x cols` `Matrix` and output is a first row of output `Matrix`.
    `<M1 extends IgniteModel<I,I>> SimpleStackedDatasetTrainer<I,O,AM,L>` | SimpleStackedDatasetTrainer.`addTrainer(DatasetTrainer<M1,L> trainer)` Adds submodel trainer along with converters needed on training and inference stages.
    `<M1 extends IgniteModel<IS,IA>> StackedDatasetTrainer<IS,IA,O,AM,L>` | StackedDatasetTrainer.`addTrainer(DatasetTrainer<M1,L> trainer)` Adds submodel trainer along with converters needed on training and inference stages.
    `<M1 extends IgniteModel<Vector,Vector>> StackedVectorDatasetTrainer<O,AM,L>` | StackedVectorDatasetTrainer.`addTrainer(DatasetTrainer<M1,L> trainer)` Adds submodel trainer along with converters needed on training and inference stages.
    `<M1 extends IgniteModel<Vector,Double>> StackedVectorDatasetTrainer<O,AM,L>` | StackedVectorDatasetTrainer.`addTrainerWithDoubleOutput(DatasetTrainer<M1,L> trainer)` Shortcut for adding trainer `Vector -> Double` where this trainer is treated as `Vector -> Vector`, where output `Vector` is constructed by wrapping double value.
    `SimpleStackedDatasetTrainer<I,O,AM,L>` | SimpleStackedDatasetTrainer.`withAggregatorTrainer(DatasetTrainer<AM,L> aggregatorTrainer)` Specify aggregator trainer.
    `StackedVectorDatasetTrainer<O,AM,L>` | StackedVectorDatasetTrainer.`withAggregatorTrainer(DatasetTrainer<AM,L> aggregatorTrainer)` Specify aggregator trainer.
    `StackedDatasetTrainer<IS,IA,O,AM,L>` | StackedDatasetTrainer.`withAggregatorTrainer(DatasetTrainer<AM,L> aggregatorTrainer)` Specify aggregator trainer.

    Constructors in org.apache.ignite.ml.composition.stacking with parameters of type DatasetTrainer
    Constructor and Description
    `SimpleStackedDatasetTrainer(DatasetTrainer<AM,L> aggregatingTrainer, IgniteBinaryOperator<I> aggregatingInputMerger)` Construct instance of this class.
    `SimpleStackedDatasetTrainer(DatasetTrainer<AM,L> aggregatingTrainer, IgniteBinaryOperator<I> aggregatingInputMerger, IgniteFunction<I,I> submodelInput2AggregatingInputConverter, IgniteFunction<Vector,I> vector2SubmodelInputConverter, IgniteFunction<I,Vector> submodelOutput2VectorConverter)` Construct instance of this class.
    `StackedDatasetTrainer(DatasetTrainer<AM,L> aggregatorTrainer, IgniteBinaryOperator<IA> aggregatingInputMerger, IgniteFunction<IS,IA> submodelInput2AggregatingInputConverter)` Constructs instance of this class.
    `StackedDatasetTrainer(DatasetTrainer<AM,L> aggregatorTrainer, IgniteBinaryOperator<IA> aggregatingInputMerger, IgniteFunction<IS,IA> submodelInput2AggregatingInputConverter, List<DatasetTrainer<IgniteModel<IS,IA>,L>> submodelsTrainers, IgniteFunction<Vector,IS> vector2SubmodelInputConverter, IgniteFunction<IA,Vector> submodelOutput2VectorConverter)` Create instance of this class.
    `StackedVectorDatasetTrainer(DatasetTrainer<AM,L> aggregatingTrainer)` Constructs instance of this class.

    Constructor parameters in org.apache.ignite.ml.composition.stacking with type arguments of type DatasetTrainer
    Constructor and Description
    `StackedDatasetTrainer(DatasetTrainer<AM,L> aggregatorTrainer, IgniteBinaryOperator<IA> aggregatingInputMerger, IgniteFunction<IS,IA> submodelInput2AggregatingInputConverter, List<DatasetTrainer<IgniteModel<IS,IA>,L>> submodelsTrainers, IgniteFunction<Vector,IS> vector2SubmodelInputConverter, IgniteFunction<IA,Vector> submodelOutput2VectorConverter)` Create instance of this class.

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

    Subclasses of DatasetTrainer in org.apache.ignite.ml.knn
    Modifier and Type | Class and Description
    --- | ---
    `class` | `KNNTrainer<M extends KNNModel<Double>,Self extends KNNTrainer<M,Self>>` KNN model trader that trains model on top of distributed spatial indices.

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

    Subclasses of DatasetTrainer in org.apache.ignite.ml.knn.ann
    Modifier and Type | Class and Description
    --- | ---
    `class` | `ANNClassificationTrainer` ANN algorithm trainer to solve multi-class classification task.

  - ### Uses of DatasetTrainer in org.apache.ignite.ml.knn.classification

    Subclasses of DatasetTrainer in org.apache.ignite.ml.knn.classification
    Modifier and Type | Class and Description
    --- | ---
    `class` | `KNNClassificationTrainer` KNN classification model trader that trains model on top of distributed spatial indices.

  - ### Uses of DatasetTrainer in org.apache.ignite.ml.knn.regression

    Subclasses of DatasetTrainer in org.apache.ignite.ml.knn.regression
    Modifier and Type | Class and Description
    --- | ---
    `class` | `KNNRegressionTrainer` KNN regression model trader that trains model on top of distributed spatial indices.

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

    Subclasses of DatasetTrainer in org.apache.ignite.ml.multiclass
    Modifier and Type | Class and Description
    --- | ---
    `class` | `OneVsRestTrainer<M extends IgniteModel<Vector,Double>>` This is a common heuristic trainer for multi-class labeled models.

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

    Subclasses of DatasetTrainer in org.apache.ignite.ml.naivebayes.compound
    Modifier and Type | Class and Description
    --- | ---
    `class` | `CompoundNaiveBayesTrainer` Trainer for the compound Naive Bayes classifier model.

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

    Subclasses of DatasetTrainer in org.apache.ignite.ml.naivebayes.discrete
    Modifier and Type | Class and Description
    --- | ---
    `class` | `DiscreteNaiveBayesTrainer` Trainer for the Discrete naive Bayes classification model.

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

    Subclasses of DatasetTrainer in org.apache.ignite.ml.naivebayes.gaussian
    Modifier and Type | Class and Description
    --- | ---
    `class` | `GaussianNaiveBayesTrainer` Trainer for the naive Bayes classification model.

  - ### Uses of DatasetTrainer in org.apache.ignite.ml.nn

    Subclasses of DatasetTrainer in org.apache.ignite.ml.nn
    Modifier and Type | Class and Description
    --- | ---
    `class` | `MLPTrainer<P extends Serializable>` Multilayer perceptron trainer based on partition based `Dataset`.

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

    Methods in org.apache.ignite.ml.pipeline that return DatasetTrainer
    Modifier and Type | Method and Description
    --- | ---
    `DatasetTrainer` | Pipeline.`getTrainer()` Returns trainer.

    Methods in org.apache.ignite.ml.pipeline with parameters of type DatasetTrainer
    Modifier and Type | Method and Description
    --- | ---
    `Pipeline<K,V,C,L>` | Pipeline.`addTrainer(DatasetTrainer trainer)` Adds a trainer.

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

    Subclasses of DatasetTrainer in org.apache.ignite.ml.regressions.linear
    Modifier and Type | Class and Description
    --- | ---
    `class` | `LinearRegressionLSQRTrainer` Trainer of the linear regression model based on LSQR algorithm.
    `class` | `LinearRegressionSGDTrainer<P extends Serializable>` Trainer of the linear regression model based on stochastic gradient descent algorithm.

  - ### Uses of DatasetTrainer in org.apache.ignite.ml.regressions.logistic

    Subclasses of DatasetTrainer in org.apache.ignite.ml.regressions.logistic
    Modifier and Type | Class and Description
    --- | ---
    `class` | `LogisticRegressionSGDTrainer` Trainer of the logistic regression model based on stochastic gradient descent algorithm.

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

    Fields in org.apache.ignite.ml.selection.cv declared as DatasetTrainer
    Modifier and Type | Field and Description
    --- | ---
    `protected DatasetTrainer<M,Double>` | AbstractCrossValidation.`trainer` Trainer.

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

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

    Subclasses of DatasetTrainer in org.apache.ignite.ml.svm
    Modifier and Type | Class and Description
    --- | ---
    `class` | `SVMLinearClassificationTrainer` Base class for a soft-margin SVM linear classification trainer based on the communication-efficient distributed dual coordinate ascent algorithm (CoCoA) with hinge-loss function.

  - ### Uses of DatasetTrainer in org.apache.ignite.ml.trainers

    Subclasses of DatasetTrainer in org.apache.ignite.ml.trainers
    Modifier and Type | Class and Description
    --- | ---
    `class` | `AdaptableDatasetTrainer<I,O,IW,OW,M extends IgniteModel<IW,OW>,L>` Type used to adapt input and output types of wrapped `DatasetTrainer`.
    `class` | `MultiLabelDatasetTrainer<M extends IgniteModel>` Interface for trainers that trains on dataset with multiple label per object.
    `class` | `SingleLabelDatasetTrainer<M extends IgniteModel>` Interface for trainers that trains on dataset with singe label per object.

    Methods in org.apache.ignite.ml.trainers that return DatasetTrainer
    Modifier and Type | Method and Description
    --- | ---
    `static <I,L> DatasetTrainer<IgniteModel<I,I>,L>` | DatasetTrainer.`identityTrainer()` Returns the trainer which returns identity model.
    `<L1> DatasetTrainer<M,L1>` | DatasetTrainer.`withConvertedLabels(IgniteFunction<L1,L> new2Old)` Creates `DatasetTrainer` with same training logic, but able to accept labels of given new type of labels.
    `DatasetTrainer<M,L>` | DatasetTrainer.`withEnvironmentBuilder(LearningEnvironmentBuilder envBuilder)` Changes learning Environment.

    Methods in org.apache.ignite.ml.trainers with parameters of type DatasetTrainer
    Modifier and Type | Method and Description
    --- | ---
    `<O1,M1 extends IgniteModel<O,O1>> TrainersSequentialComposition<I,O,O1,L>` | AdaptableDatasetTrainer.`andThen(DatasetTrainer<M1,L> tr, IgniteFunction<AdaptableDatasetModel<I,O,IW,OW,M>,IgniteFunction<LabeledVector<L>,LabeledVector<L>>> datasetMappingProducer)` Create a `TrainersSequentialComposition` of this trainer and specified trainer.
    `static <L> BaggedTrainer<L>` | TrainerTransformers.`makeBagged(DatasetTrainer<? extends IgniteModel,L> trainer, int ensembleSize, double subsampleRatio, PredictionsAggregator aggregator)` Add bagging logic to a given trainer.
    `static <M extends IgniteModel<Vector,Double>,L> BaggedTrainer<L>` | TrainerTransformers.`makeBagged(DatasetTrainer<M,L> trainer, int ensembleSize, double subsampleRatio, int featureVectorSize, int featuresSubspaceDim, PredictionsAggregator aggregator)` Add bagging logic to a given trainer.
    `static <I,O,M extends IgniteModel<I,O>,L> AdaptableDatasetTrainer<I,O,I,O,M,L>` | AdaptableDatasetTrainer.`of(DatasetTrainer<M,L> wrapped)` Construct instance of this class from a given `DatasetTrainer`.

  - ### Uses of DatasetTrainer in org.apache.ignite.ml.tree

    Subclasses of DatasetTrainer in org.apache.ignite.ml.tree
    Modifier and Type | Class and Description
    --- | ---
    `class` | `DecisionTree<T extends ImpurityMeasure<T>>` Distributed decision tree trainer that allows to fit trees using row-partitioned dataset.
    `class` | `DecisionTreeClassificationTrainer` Decision tree classifier based on distributed decision tree trainer that allows to fit trees using row-partitioned dataset.
    `class` | `DecisionTreeRegressionTrainer` Decision tree regressor based on distributed decision tree trainer that allows to fit trees using row-partitioned dataset.

  - ### Uses of DatasetTrainer in org.apache.ignite.ml.tree.boosting

    Subclasses of DatasetTrainer in org.apache.ignite.ml.tree.boosting
    Modifier and Type | Class and Description
    --- | ---
    `class` | `GDBBinaryClassifierOnTreesTrainer` Implementation of Gradient Boosting Classifier Trainer on trees.
    `class` | `GDBRegressionOnTreesTrainer` Implementation of Gradient Boosting Regression Trainer on trees.

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

    Subclasses of DatasetTrainer in org.apache.ignite.ml.tree.randomforest
    Modifier and Type | Class and Description
    --- | ---
    `class` | `RandomForestClassifierTrainer` Classifier trainer based on RandomForest algorithm.
    `class` | `RandomForestRegressionTrainer` Regression trainer based on RandomForest algorithm.
    `class` | `RandomForestTrainer<L,S extends ImpurityComputer<BootstrappedVector,S>,T extends RandomForestTrainer<L,S,T>>` Class represents a realization of Random Forest algorithm.

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