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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