[GitHub] spark pull request: [SPARK-15100][DOC] Modified user guide and examples for ...

[oliverpierson <gi...@git.apache.org>]

Github user oliverpierson commented on a diff in the pull request:

    https://github.com/apache/spark/pull/13176#discussion_r65173690
  
    --- Diff: docs/ml-features.md ---
    @@ -145,9 +148,11 @@ for more details on the API.
      passed to other algorithms like LDA.
     
      During the fitting process, `CountVectorizer` will select the top `vocabSize` words ordered by
    - term frequency across the corpus. An optional parameter "minDF" also affects the fitting process
    + term frequency across the corpus. An optional parameter `minDF` also affects the fitting process
      by specifying the minimum number (or fraction if < 1.0) of documents a term must appear in to be
    - included in the vocabulary.
    + included in the vocabulary. Another optional binary toggle parameter controls the output vector.
    --- End diff --
    
    The difference in results is a bit puzzling.  I'm getting the same thing as @MLnick.  Could you both look at the output of `df.stat.approxQuantile("hour", Array(1.0/3, 2.0/3), relativeError=0.001)`.  I get the following on the DataFrame above:
    
    ```
    scala> df.stat.approxQuantile("hour", Array(1.0/3, 2.0/3), relativeError=0.001)
    res8: Array[Double] = Array(2.2, 5.0)
    ```



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