Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/15428#discussion_r84563237
--- Diff: python/pyspark/ml/feature.py ---
@@ -1157,9 +1157,11 @@ class QuantileDiscretizer(JavaEstimator,
HasInputCol, HasOutputCol, JavaMLReadab
categorical features. The number of bins can be set using the
:py:attr:`numBuckets` parameter.
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. Note also
- that NaN values are handled specially and placed into their own
bucket. For example, if 4
- buckets are used, then non-NaN data will be put into buckets(0-3), but
NaNs will be counted in
- a special bucket(4).
+ that QuantileDiscretizer will raise an error when it finds NaN value
in the dataset, but user
--- End diff --
Actually no need to update Python API until it is updated to include
handleNaN
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