SparkQA commented on issue #25929: [SPARK-29116][PYTHON][ML] Refactor py classes related to DecisionTree URL: https://github.com/apache/spark/pull/25929#issuecomment-541278850 **[Test build #111947 has finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/111947/testReport)** for PR 25929 at commit [`302e98e`](https://github.com/apache/spark/commit/302e98eb8fc9cd8e776b7b6d8507f256ac6b31a2). * This patch passes all tests. * This patch merges cleanly. * This patch adds the following public classes _(experimental)_: * `class _DecisionTreeClassifierParams(_DecisionTreeParams, _TreeClassifierParams):` * `class DecisionTreeClassificationModel(_DecisionTreeModel, JavaProbabilisticClassificationModel,` * `class _RandomForestClassifierParams(_RandomForestParams, _TreeClassifierParams):` * `class RandomForestClassificationModel(_TreeEnsembleModel, JavaProbabilisticClassificationModel,` * `class GBTClassifierParams(_GBTParams, _HasVarianceImpurity):` * `class GBTClassificationModel(_TreeEnsembleModel, JavaProbabilisticClassificationModel,` * `class _DecisionTreeRegressorParams(_DecisionTreeParams, _TreeRegressorParams, HasVarianceCol):` * `class DecisionTreeRegressionModel(_DecisionTreeModel, _DecisionTreeRegressorParams,` * `class _RandomForestRegressorParams(_RandomForestParams, _TreeRegressorParams):` * `class RandomForestRegressionModel(_TreeEnsembleModel, _RandomForestRegressorParams,` * `class _GBTRegressorParams(_GBTParams, _TreeRegressorParams):` * `class GBTRegressor(JavaPredictor, _GBTRegressorParams, JavaMLWritable, JavaMLReadable):` * `class GBTRegressionModel(_TreeEnsembleModel, _GBTRegressorParams, JavaMLWritable, JavaMLReadable):` * `class _DecisionTreeModel(JavaPredictionModel):` * `class _DecisionTreeParams(HasCheckpointInterval, HasSeed, HasWeightCol):` * `class _TreeEnsembleModel(JavaPredictionModel):` * `class _TreeEnsembleParams(_DecisionTreeParams):` * `class _RandomForestParams(_TreeEnsembleParams):` * `class _GBTParams(_TreeEnsembleParams, HasMaxIter, HasStepSize, HasValidationIndicatorCol):` * `class _HasVarianceImpurity(Params):` * `class _TreeClassifierParams(object):` * `class _TreeRegressorParams(_HasVarianceImpurity):`
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