Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/16694#discussion_r98141074
--- Diff: python/pyspark/ml/classification.py ---
@@ -60,6 +61,137 @@ def numClasses(self):
@inherit_doc
+class LinearSVC(JavaEstimator, HasFeaturesCol, HasLabelCol,
HasPredictionCol, HasMaxIter,
+ HasRegParam, HasTol, HasRawPredictionCol, HasFitIntercept,
HasStandardization,
+ HasThreshold, HasWeightCol, HasAggregationDepth,
JavaMLWritable, JavaMLReadable):
+ """
+ Linear SVM Classifier
(https://en.wikipedia.org/wiki/Support_vector_machine#Linear_SVM)
+ This binary classifier optimizes the Hinge Loss using the OWLQN
optimizer.
+
+ >>> from pyspark.sql import Row
+ >>> from pyspark.ml.linalg import Vectors
+ >>> bdf = sc.parallelize([
+ ... Row(label=1.0, weight=2.0, features=Vectors.dense(1.0)),
--- End diff --
I'd simplify this example since it is going to be part of the documentation:
* Remove "weight"
* Just use dense vectors to make the doc clearer. Sparse vectors are
tested elsewhere for Python and should be tested in Scala for LinearSVC (for
which I'll make a JIRA).
* Make the feature vectors be length 2 or 3
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