Github user BryanCutler commented on a diff in the pull request:
https://github.com/apache/spark/pull/10601#discussion_r51619200
--- Diff: python/pyspark/mllib/tree.py ---
@@ -305,31 +326,39 @@ def trainClassifier(cls, data, numClasses,
categoricalFeaturesInfo, numTrees,
Method to train a decision tree model for binary or multiclass
classification.
- :param data: Training dataset: RDD of LabeledPoint. Labels
- should take values {0, 1, ..., numClasses-1}.
- :param numClasses: number of classes for classification.
- :param categoricalFeaturesInfo: Map storing arity of categorical
- features. E.g., an entry (n -> k) indicates that
- feature n is categorical with k categories indexed
- from 0: {0, 1, ..., k-1}.
- :param numTrees: Number of trees in the random forest.
- :param featureSubsetStrategy: Number of features to consider for
- splits at each node.
- Supported: "auto" (default), "all", "sqrt", "log2",
"onethird".
- If "auto" is set, this parameter is set based on numTrees:
- if numTrees == 1, set to "all";
- if numTrees > 1 (forest) set to "sqrt".
- :param impurity: Criterion used for information gain calculation.
- Supported values: "gini" (recommended) or "entropy".
- :param maxDepth: Maximum depth of the tree.
- E.g., depth 0 means 1 leaf node; depth 1 means
- 1 internal node + 2 leaf nodes. (default: 4)
- :param maxBins: maximum number of bins used for splitting
- features
- (default: 32)
- :param seed: Random seed for bootstrapping and choosing feature
- subsets.
- :return: RandomForestModel that can be used for prediction
+ :param data:
+ Training dataset: RDD of LabeledPoint. Labels should take values
+ {0, 1, ..., numClasses-1}.:param numClasses: Number of classes
--- End diff --
the `numClasses` param got put in with the previous description
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