## Uses of Interface
org.apache.ignite.ml.tree.randomforest.data.impurity.ImpurityComputer

- Packages that use ImpurityComputer
  Package | Description
  --- | ---
  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.

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

    Classes in org.apache.ignite.ml.tree.randomforest with type parameters of type ImpurityComputer
    Modifier and Type | Class and Description
    --- | ---
    `class` | `RandomForestTrainer<L,S extends ImpurityComputer<BootstrappedVector,S>,T extends RandomForestTrainer<L,S,T>>` Class represents a realization of Random Forest algorithm.

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

    Classes in org.apache.ignite.ml.tree.randomforest.data.impurity with type parameters of type ImpurityComputer
    Modifier and Type | Class and Description
    --- | ---
    `class` | `ImpurityHistogramsComputer<S extends ImpurityComputer<BootstrappedVector,S>>` Class containing logic of aggregation impurity statistics within learning dataset.
    `static class` | `ImpurityHistogramsComputer.NodeImpurityHistograms<S extends ImpurityComputer<BootstrappedVector,S>>` Class represents per feature statistics for impurity computing.

    Classes in org.apache.ignite.ml.tree.randomforest.data.impurity that implement ImpurityComputer
    Modifier and Type | Class and Description
    --- | ---
    `class` | `GiniHistogram` Class contains implementation of splitting point finding algorithm based on Gini metric (see https://en.wikipedia.org/wiki/Gini_coefficient) and represents a set of histograms in according to this metric.
    `class` | `MSEHistogram` Class contains implementation of splitting point finding algorithm based on MSE metric (see https://en.wikipedia.org/wiki/Mean_squared_error) and represents a set of histograms in according to this metric.

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Release Date | : October 15 2020