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Diffstat (limited to 'python/pyspark/ml/feature.py')
-rwxr-xr-x | python/pyspark/ml/feature.py | 5 |
1 files changed, 0 insertions, 5 deletions
diff --git a/python/pyspark/ml/feature.py b/python/pyspark/ml/feature.py index 7683360664..94afe82a36 100755 --- a/python/pyspark/ml/feature.py +++ b/python/pyspark/ml/feature.py @@ -1155,11 +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 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). 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 |