## Uses of Class
org.apache.ignite.ml.dataset.impl.bootstrapping.BootstrappedDatasetPartition

- Packages that use BootstrappedDatasetPartition
  Package | Description
  --- | ---
  org.apache.ignite.ml.dataset.impl.bootstrapping | Base package for bootstrapped implementation of machine learning dataset.
  org.apache.ignite.ml.tree.randomforest | Contains random forest implementation classes.
  org.apache.ignite.ml.tree.randomforest.data.impurity | Contains implementation of impurity computers based on histograms.
  org.apache.ignite.ml.tree.randomforest.data.statistics | Contains implementation of statistics computers for Random Forest.

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

    Methods in org.apache.ignite.ml.dataset.impl.bootstrapping that return BootstrappedDatasetPartition
    Modifier and Type | Method and Description
    --- | ---
    `BootstrappedDatasetPartition` | BootstrappedDatasetBuilder.`build(LearningEnvironment env, Iterator<UpstreamEntry<K,V>> upstreamData, long upstreamDataSize, EmptyContext ctx)` Builds a new partition `data` from a partition `upstream` data and partition `context`.

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

    Method parameters in org.apache.ignite.ml.tree.randomforest with type arguments of type BootstrappedDatasetPartition
    Modifier and Type | Method and Description
    --- | ---
    `protected boolean` | RandomForestClassifierTrainer.`init(Dataset<EmptyContext,BootstrappedDatasetPartition> dataset)` Aggregates all unique labels from dataset and assigns integer id value for each label.
    `protected boolean` | RandomForestTrainer.`init(Dataset<EmptyContext,BootstrappedDatasetPartition> dataset)` Init-step before learning.

  - ### Uses of BootstrappedDatasetPartition in org.apache.ignite.ml.tree.randomforest.data.impurity

    Method parameters in org.apache.ignite.ml.tree.randomforest.data.impurity with type arguments of type BootstrappedDatasetPartition
    Modifier and Type | Method and Description
    --- | ---
    `Map<NodeId,ImpurityHistogramsComputer.NodeImpurityHistograms<S>>` | ImpurityHistogramsComputer.`aggregateImpurityStatistics(ArrayList<RandomForestTreeModel> roots, Map<Integer,BucketMeta> histMeta, Map<NodeId,TreeNode> nodesToLearn, Dataset<EmptyContext,BootstrappedDatasetPartition> dataset)` Computes histograms for each feature.

  - ### Uses of BootstrappedDatasetPartition in org.apache.ignite.ml.tree.randomforest.data.statistics

    Methods in org.apache.ignite.ml.tree.randomforest.data.statistics with parameters of type BootstrappedDatasetPartition
    Modifier and Type | Method and Description
    --- | ---
    `List<NormalDistributionStatistics>` | NormalDistributionStatisticsComputer.`computeStatsOnPartition(BootstrappedDatasetPartition part, List<FeatureMeta> meta)` Aggregates normal distribution statistics for continual features in dataset partition.

    Method parameters in org.apache.ignite.ml.tree.randomforest.data.statistics with type arguments of type BootstrappedDatasetPartition
    Modifier and Type | Method and Description
    --- | ---
    `List<NormalDistributionStatistics>` | NormalDistributionStatisticsComputer.`computeStatistics(List<FeatureMeta> meta, Dataset<EmptyContext,BootstrappedDatasetPartition> dataset)` Computes statistics of normal distribution on features in dataset.
    `void` | LeafValuesComputer.`setValuesForLeaves(ArrayList<RandomForestTreeModel> roots, Dataset<EmptyContext,BootstrappedDatasetPartition> dataset)` Takes a list of all built trees and in one map-reduceImpurityStatistics step collect statistics for evaluating leaf-values for each tree and sets values for leaves.

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Release Date | : September 11 2021