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  1. Spark
  2. SPARK-24609

PySpark/SparkR doc doesn't explain RandomForestClassifier.featureSubsetStrategy well

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    • Type: Improvement
    • Status: Resolved
    • Priority: Minor
    • Resolution: Fixed
    • Affects Version/s: 2.3.1
    • Fix Version/s: 2.4.0
    • Component/s: PySpark
    • Labels:
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    • Target Version/s:

      Description

      In Scala doc (https://spark.apache.org/docs/2.3.0/api/scala/index.html#org.apache.spark.ml.classification.RandomForestClassifier), we have:

       

      The number of features to consider for splits at each tree node. Supported options:

      • "auto": Choose automatically for task: If numTrees == 1, set to "all." If numTrees > 1 (forest), set to "sqrt" for classification and to "onethird" for regression.
      • "all": use all features
      • "onethird": use 1/3 of the features
      • "sqrt": use sqrt(number of features)
      • "log2": use log2(number of features)
      • "n": when n is in the range (0, 1.0], use n * number of features. When n is in the range (1, number of features), use n features. (default = "auto")

      These various settings are based on the following references:

      • log2: tested in Breiman (2001)
      • sqrt: recommended by Breiman manual for random forests
      • The defaults of sqrt (classification) and onethird (regression) match the R randomForest package.

       

      The entire paragraph is missing in PySpark doc (https://spark.apache.org/docs/2.3.0/api/python/pyspark.ml.html#pyspark.ml.classification.RandomForestClassifier.featureSubsetStrategy). And same issue for SparkR (https://github.com/apache/spark/blob/master/R/pkg/R/mllib_tree.R#L365).

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            • Assignee:
              podongfeng zhengruifeng
              Reporter:
              mengxr Xiangrui Meng
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