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| author | Zheng RuiFeng <ruifengz@foxmail.com> | 2017-03-21 08:45:59 -0700 |
|---|---|---|
| committer | Xiao Li <gatorsmile@gmail.com> | 2017-03-21 08:45:59 -0700 |
| commit | 63f077fbe50b4094340e9915db41d7dbdba52975 (patch) | |
| tree | 3442fa7374aa58b648de5c5bb4c76a5e3a9769df /mllib/src | |
| parent | 14865d7ff78db5cf9a3e8626204c8e7ed059c353 (diff) | |
| download | spark-63f077fbe50b4094340e9915db41d7dbdba52975.tar.gz spark-63f077fbe50b4094340e9915db41d7dbdba52975.tar.bz2 spark-63f077fbe50b4094340e9915db41d7dbdba52975.zip | |
[SPARK-20041][DOC] Update docs for NaN handling in approxQuantile
## What changes were proposed in this pull request?
Update docs for NaN handling in approxQuantile.
## How was this patch tested?
existing tests.
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes #17369 from zhengruifeng/doc_quantiles_nan.
Diffstat (limited to 'mllib/src')
| -rw-r--r-- | mllib/src/main/scala/org/apache/spark/ml/feature/QuantileDiscretizer.scala | 4 |
1 files changed, 2 insertions, 2 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/QuantileDiscretizer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/QuantileDiscretizer.scala index 80c7f55e26..feceeba866 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/QuantileDiscretizer.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/QuantileDiscretizer.scala @@ -93,8 +93,8 @@ private[feature] trait QuantileDiscretizerBase extends Params * are too few distinct values of the input to create enough distinct quantiles. * * NaN handling: - * NaN values will be removed from the column during `QuantileDiscretizer` fitting. This will - * produce a `Bucketizer` model for making predictions. During the transformation, + * null and NaN values will be ignored from the column during `QuantileDiscretizer` fitting. This + * will produce a `Bucketizer` model for making predictions. During the transformation, * `Bucketizer` 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 placed into their own |