Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/12066#discussion_r61474440
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala ---
@@ -62,6 +92,14 @@ abstract class Classifier[
/** @group setParam */
def setRawPredictionCol(value: String): E = set(rawPredictionCol,
value).asInstanceOf[E]
+ override def fit(dataset: Dataset[_]): M = {
+ // This handles a few items such as schema validation.
+ // Developers only need to implement train().
+ transformSchema(dataset.schema, logging = true)
+ copyValues(train(dataset).setParent(this))
+ .setPredictionMetadata(generatePredictionMetadata(dataset.schema))
--- End diff --
Here, you could pass in numClasses, which is now available from the model.
generatePredictionMetadata could take an optional numClasses argument.
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