[GitHub] spark pull request #16922: [SPARK-19590][pyspark][ML] Update the document fo...

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

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

    https://github.com/apache/spark/pull/16922#discussion_r101097983
  
    --- Diff: python/pyspark/ml/feature.py ---
    @@ -1178,7 +1178,17 @@ class QuantileDiscretizer(JavaEstimator, HasInputCol, HasOutputCol, JavaMLReadab
     
         `QuantileDiscretizer` takes a column with continuous features and outputs a column with binned
         categorical features. The number of bins can be set using the :py:attr:`numBuckets` parameter.
    -    The bin ranges are chosen using an approximate algorithm (see the documentation for
    +    It is possible that the number of buckets used will be less than this value, for example, if
    +    there are too few distinct values of the input to create enough distinct quantiles.
    +
    +    NaN handling: Note also that
    +    QuantileDiscretizer will raise an error when it finds NaN values in the dataset, but the user
    +    can also choose to either keep or remove NaN values within the dataset by setting
    +    `handleInvalid`. If the user chooses to keep NaN values, they will be handled specially and
    --- End diff --
    
    could we maybe link this with a py attr like we did with numBuckets?


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