## Uses of Class
org.apache.ignite.ml.composition.ModelsComposition

- Packages that use ModelsComposition
  Package | Description
  --- | ---
  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.tree.randomforest | Contains random forest implementation classes.
  org.apache.ignite.ml.xgboost | Base package for XGBoost model parser, correspondent DTOs and util classes.

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

    Subclasses of ModelsComposition in org.apache.ignite.ml.composition.boosting
    Modifier and Type | Class and Description
    --- | ---
    `static class` | `GDBTrainer.GDBModel` GDB model.

    Methods in org.apache.ignite.ml.composition.boosting that return ModelsComposition
    Modifier and Type | Method and Description
    --- | ---
    `<K,V> ModelsComposition` | GDBTrainer.`fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> preprocessor)` Trains model based on the specified data.
    `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.

    Methods in org.apache.ignite.ml.composition.boosting with parameters of type ModelsComposition
    Modifier and Type | Method and Description
    --- | ---
    `boolean` | GDBTrainer.`isUpdateable(ModelsComposition mdl)`
    `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 ModelsComposition in org.apache.ignite.ml.composition.boosting.convergence

    Methods in org.apache.ignite.ml.composition.boosting.convergence with parameters of type ModelsComposition
    Modifier and Type | Method and Description
    --- | ---
    `double` | ConvergenceChecker.`computeError(Vector features, Double answer, ModelsComposition currMdl)` Compute error for the specific vector of dataset.
    `abstract Double` | ConvergenceChecker.`computeMeanErrorOnDataset(Dataset<EmptyContext,? extends FeatureMatrixWithLabelsOnHeapData> dataset, ModelsComposition mdl)` Compute error for given model on learning dataset.
    `boolean` | ConvergenceChecker.`isConverged(Dataset<EmptyContext,? extends FeatureMatrixWithLabelsOnHeapData> dataset, ModelsComposition currMdl)` Checks convergency on dataset.
    `boolean` | ConvergenceChecker.`isConverged(LearningEnvironmentBuilder envBuilder, DatasetBuilder<K,V> datasetBuilder, ModelsComposition currMdl)` Checks convergency on dataset.

  - ### Uses of ModelsComposition in org.apache.ignite.ml.composition.boosting.convergence.mean

    Methods in org.apache.ignite.ml.composition.boosting.convergence.mean with parameters of type ModelsComposition
    Modifier and Type | Method and Description
    --- | ---
    `Double` | MeanAbsValueConvergenceChecker.`computeMeanErrorOnDataset(Dataset<EmptyContext,? extends FeatureMatrixWithLabelsOnHeapData> dataset, ModelsComposition mdl)` Compute error for given model on learning dataset.

  - ### Uses of ModelsComposition in org.apache.ignite.ml.composition.boosting.convergence.median

    Methods in org.apache.ignite.ml.composition.boosting.convergence.median with parameters of type ModelsComposition
    Modifier and Type | Method and Description
    --- | ---
    `Double` | MedianOfMedianConvergenceChecker.`computeMeanErrorOnDataset(Dataset<EmptyContext,? extends FeatureMatrixWithLabelsOnHeapData> dataset, ModelsComposition mdl)` Compute error for given model on learning dataset.

  - ### Uses of ModelsComposition in org.apache.ignite.ml.composition.boosting.convergence.simple

    Methods in org.apache.ignite.ml.composition.boosting.convergence.simple with parameters of type ModelsComposition
    Modifier and Type | Method and Description
    --- | ---
    `Double` | ConvergenceCheckerStub.`computeMeanErrorOnDataset(Dataset<EmptyContext,? extends FeatureMatrixWithLabelsOnHeapData> dataset, ModelsComposition mdl)` Compute error for given model on learning dataset.
    `boolean` | ConvergenceCheckerStub.`isConverged(Dataset<EmptyContext,? extends FeatureMatrixWithLabelsOnHeapData> dataset, ModelsComposition currMdl)` Checks convergency on dataset.
    `boolean` | ConvergenceCheckerStub.`isConverged(LearningEnvironmentBuilder envBuilder, DatasetBuilder<K,V> datasetBuilder, ModelsComposition currMdl)` Checks convergency on dataset.

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

    Methods in org.apache.ignite.ml.tree.randomforest that return ModelsComposition
    Modifier and Type | Method and Description
    --- | ---
    `protected abstract ModelsComposition` | RandomForestTrainer.`buildComposition(List<TreeRoot> models)` Returns composition of built trees.
    `protected ModelsComposition` | RandomForestRegressionTrainer.`buildComposition(List<TreeRoot> models)` Returns composition of built trees.
    `protected ModelsComposition` | RandomForestClassifierTrainer.`buildComposition(List<TreeRoot> models)` Returns composition of built trees.
    `<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.

    Methods in org.apache.ignite.ml.tree.randomforest with parameters of type ModelsComposition
    Modifier and Type | Method and Description
    --- | ---
    `boolean` | RandomForestTrainer.`isUpdateable(ModelsComposition mdl)`
    `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 ModelsComposition in org.apache.ignite.ml.xgboost

    Methods in org.apache.ignite.ml.xgboost that return ModelsComposition
    Modifier and Type | Method and Description
    --- | ---
    `ModelsComposition` | XGModelComposition.`getModelsComposition()`

    Methods in org.apache.ignite.ml.xgboost with parameters of type ModelsComposition
    Modifier and Type | Method and Description
    --- | ---
    `void` | XGModelComposition.`setModelsComposition(ModelsComposition modelsComposition)`

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