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| author | wm624@hotmail.com <wm624@hotmail.com> | 2017-01-27 16:03:53 -0800 |
|---|---|---|
| committer | Joseph K. Bradley <joseph@databricks.com> | 2017-01-27 16:03:53 -0800 |
| commit | bb1a1fe05e293c480c88123d4c83a6b8c25f6e2e (patch) | |
| tree | 3052e47ad7cae1badb4257fb75eea3a1dbbdab68 /mllib/src | |
| parent | 21aa8c32ba7a29aafc000ecce2e6c802ced6a009 (diff) | |
| download | spark-bb1a1fe05e293c480c88123d4c83a6b8c25f6e2e.tar.gz spark-bb1a1fe05e293c480c88123d4c83a6b8c25f6e2e.tar.bz2 spark-bb1a1fe05e293c480c88123d4c83a6b8c25f6e2e.zip | |
[SPARK-19336][ML][PYSPARK] LinearSVC Python API
## What changes were proposed in this pull request?
Add Python API for the newly added LinearSVC algorithm.
## How was this patch tested?
Add new doc string test.
Author: wm624@hotmail.com <wm624@hotmail.com>
Closes #16694 from wangmiao1981/ser.
Diffstat (limited to 'mllib/src')
| -rw-r--r-- | mllib/src/main/scala/org/apache/spark/ml/classification/LinearSVC.scala | 4 |
1 files changed, 2 insertions, 2 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/LinearSVC.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/LinearSVC.scala index c4e93bf5e8..3b14c4b004 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/classification/LinearSVC.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/classification/LinearSVC.scala @@ -114,7 +114,7 @@ class LinearSVC @Since("2.2.0") ( setDefault(standardization -> true) /** - * Sets the value of param [[weightCol]]. + * Set the value of param [[weightCol]]. * If this is not set or empty, we treat all instance weights as 1.0. * Default is not set, so all instances have weight one. * @@ -421,7 +421,7 @@ private class LinearSVCCostFun( /** * LinearSVCAggregator computes the gradient and loss for hinge loss function, as used - * in binary classification for instances in sparse or dense vector in a online fashion. + * in binary classification for instances in sparse or dense vector in an online fashion. * * Two LinearSVCAggregator can be merged together to have a summary of loss and gradient of * the corresponding joint dataset. |