## Uses of Class
org.apache.ignite.ml.composition.stacking.SimpleStackedDatasetTrainer

- Packages that use SimpleStackedDatasetTrainer
  Package | Description
  --- | ---
  org.apache.ignite.ml.composition.stacking | Contains classes used for training with stacking technique.

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

    Subclasses of SimpleStackedDatasetTrainer in org.apache.ignite.ml.composition.stacking
    Modifier and Type | Class and Description
    --- | ---
    `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 that return SimpleStackedDatasetTrainer
    Modifier and Type | Method and Description
    --- | ---
    `<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.
    `SimpleStackedDatasetTrainer<I,O,AM,L>` | SimpleStackedDatasetTrainer.`withAggregatorInputMerger(IgniteBinaryOperator<I> merger)` Specify binary operator used to merge submodels outputs to one.
    `SimpleStackedDatasetTrainer<I,O,AM,L>` | SimpleStackedDatasetTrainer.`withAggregatorTrainer(DatasetTrainer<AM,L> aggregatorTrainer)` Specify aggregator trainer.
    `<L1> SimpleStackedDatasetTrainer<I,O,AM,L1>` | SimpleStackedDatasetTrainer.`withConvertedLabels(IgniteFunction<L1,L> new2Old)` Creates `DatasetTrainer` with same training logic, but able to accept labels of given new type of labels.
    `SimpleStackedDatasetTrainer<I,O,AM,L>` | SimpleStackedDatasetTrainer.`withEnvironmentBuilder(LearningEnvironmentBuilder envBuilder)` Changes learning Environment.
    `SimpleStackedDatasetTrainer<I,O,AM,L>` | SimpleStackedDatasetTrainer.`withOriginalFeaturesDropped()` Drop original features during training and inference.
    `SimpleStackedDatasetTrainer<I,O,AM,L>` | SimpleStackedDatasetTrainer.`withOriginalFeaturesKept()` Keep original features using `IgniteFunction.identity()` as submodelInput2AggregatingInputConverter.
    `SimpleStackedDatasetTrainer<I,O,AM,L>` | SimpleStackedDatasetTrainer.`withOriginalFeaturesKept(IgniteFunction<I,I> submodelInput2AggregatingInputConverter)` Keep original features during training and propagate submodels input to aggregator during inference using given function.

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Ignite Database and Caching Platform | : ver. 2.9.1
Release Date | : December 9 2020