Github user BryanCutler commented on a diff in the pull request:
https://github.com/apache/spark/pull/10601#discussion_r50582414
--- Diff: python/pyspark/mllib/tree.py ---
@@ -545,28 +587,35 @@ def trainRegressor(cls, data, categoricalFeaturesInfo,
"""
Method to train a gradient-boosted trees model for regression.
- :param data: Training dataset: RDD of LabeledPoint. Labels are
- real numbers.
- :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 loss: Loss function used for minimization during gradient
- boosting. Supported: {"logLoss" (default),
- "leastSquaresError", "leastAbsoluteError"}.
- :param numIterations: Number of iterations of boosting.
- (default: 100)
- :param learningRate: Learning rate for shrinking the
- contribution of each estimator. The learning rate
- should be between in the interval (0, 1].
- (default: 0.1)
- :param maxBins: maximum number of bins used for splitting
- features (default: 32) DecisionTree requires maxBins >=
max categories
- :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: 3)
+ :param data:
+ Training dataset: RDD of LabeledPoint. Labels are real numbers.
+ :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 loss:
+ Loss function used for minimization during gradient boosting.
+ Supported values: {"logLoss", "leastSquaresError",
+ "leastAbsoluteError"}.
+ (default: "logLoss")
+ :param numIterations:
+ Number of iterations of boosting.
+ (default: 100)
+ :param learningRate:
+ Learning rate for shrinking the contribution of each estimator.
+ The learning rate should be between in the interval (0, 1].
+ (default: 0.1)
+ :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: 3)
+ :param maxBins:
+ Maximum number of bins used for splitting features. DecisionTree
+ requires maxBins >= max categories
+ (default: 32)
+
:return: GradientBoostedTreesModel that can be used for
- prediction
+ prediction
--- End diff --
remove blank line and :return: formatting
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