revert changes in feature.py

Signed-off-by: VinceShieh <[email protected]>

Author: VinceShieh

python/pyspark/ml/feature.py

@@ -1155,13 +1155,6 @@ 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.
-    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 QuantileDiscretizer will raise an error when it finds NaN value in the dataset, but user
-    can also choose to either keep or remove NaN values within the dataset by setting
-    handleInvalid. If user chooses to keep NaN values, they will be 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].
     The bin ranges are chosen using an approximate algorithm (see the documentation for
     :py:meth:`~.DataFrameStatFunctions.approxQuantile` for a detailed description).
     The precision of the approximation can be controlled with the