diff options
| -rwxr-xr-x | python/pyspark/ml/feature.py | 12 |
1 files changed, 11 insertions, 1 deletions
diff --git a/python/pyspark/ml/feature.py b/python/pyspark/ml/feature.py index ac90c899d9..1ab42919ea 100755 --- a/python/pyspark/ml/feature.py +++ b/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 + :py:attr:`handleInvalid` parameter. If the 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]. + + Algorithm: 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 :py:attr:`relativeError` parameter. |