Github user hhbyyh commented on a diff in the pull request:
https://github.com/apache/spark/pull/17645#discussion_r113836900
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/classification/LinearSVC.scala ---
@@ -42,15 +44,35 @@ import org.apache.spark.sql.functions.{col, lit}
/** Params for linear SVM Classifier. */
private[classification] trait LinearSVCParams extends ClassifierParams
with HasRegParam
with HasMaxIter with HasFitIntercept with HasTol with HasStandardization
with HasWeightCol
- with HasThreshold with HasAggregationDepth
+ with HasThreshold with HasAggregationDepth {
+
+ /**
+ * Specifies the loss function. Currently "hinge" and "squared_hinge"
are supported.
+ * "hinge" is the standard SVM loss (a.k.a. L1 loss) while
"squared_hinge" is the square of
+ * the hinge loss (a.k.a. L2 loss).
+ *
+ * @see <a href="https://en.wikipedia.org/wiki/Hinge_loss";>Hinge loss
(Wikipedia)</a>
+ *
+ * @group param
+ */
+ @Since("2.3.0")
+ final val lossFunction: Param[String] = new Param(this, "lossFunction",
"Specifies the loss " +
--- End diff --
Sure we can do it.
But I'm thinking maybe we should conduct an integrated refactor about the
common optimization parameters some time in the future, either through shared
params or other trait or abstract class.
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