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

- Packages that use org.apache.ignite.ml.tree.randomforest.data.impurity
  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.

- Classes in org.apache.ignite.ml.tree.randomforest.data.impurity used by org.apache.ignite.ml.tree.randomforest
  Class and Description
  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.
  ImpurityComputer Interface represents an object that can compute best splitting point using features histograms.
  ImpurityHistogramsComputer Class containing logic of aggregation impurity statistics within learning dataset.
  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.

- Classes in org.apache.ignite.ml.tree.randomforest.data.impurity used by org.apache.ignite.ml.tree.randomforest.data.impurity
  Class and Description
  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.
  ImpurityComputer Interface represents an object that can compute best splitting point using features histograms.
  ImpurityHistogram Helper class for ImpurityHistograms.
  ImpurityHistogramsComputer Class containing logic of aggregation impurity statistics within learning dataset.
  ImpurityHistogramsComputer.NodeImpurityHistograms Class represents per feature statistics for impurity computing.
  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 | : December 9 2020