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| author | VinceShieh <vincent.xie@intel.com> | 2017-02-15 10:12:07 -0800 |
|---|---|---|
| committer | Holden Karau <holden@us.ibm.com> | 2017-02-15 10:12:07 -0800 |
| commit | 6eca21ba881120f1ac7854621380ef8a92972384 (patch) | |
| tree | 6185cae2b129369cea41149141af37592cc16a4c /python | |
| parent | acf71c63cdde8dced8d108260cdd35e1cc992248 (diff) | |
| download | spark-6eca21ba881120f1ac7854621380ef8a92972384.tar.gz spark-6eca21ba881120f1ac7854621380ef8a92972384.tar.bz2 spark-6eca21ba881120f1ac7854621380ef8a92972384.zip | |
[SPARK-19590][PYSPARK][ML] Update the document for QuantileDiscretizer in pyspark
## What changes were proposed in this pull request?
This PR is to document the changes on QuantileDiscretizer in pyspark for PR:
https://github.com/apache/spark/pull/15428
## How was this patch tested?
No test needed
Signed-off-by: VinceShieh <vincent.xieintel.com>
Author: VinceShieh <vincent.xie@intel.com>
Closes #16922 from VinceShieh/spark-19590.
Diffstat (limited to 'python')
| -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. |