Github user VinceShieh commented on a diff in the pull request: https://github.com/apache/spark/pull/16922#discussion_r101183452 --- 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 -- yeah, sure. Thanks for pointing that out... ;)
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