Github user manishamde commented on a diff in the pull request:
https://github.com/apache/spark/pull/1582#discussion_r15498862
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala ---
@@ -19,48 +19,60 @@ package org.apache.spark.mllib.tree
import org.apache.spark.annotation.Experimental
import org.apache.spark.Logging
+import org.apache.spark.mllib.rdd.DatasetInfo
import org.apache.spark.mllib.regression.LabeledPoint
-import org.apache.spark.mllib.tree.configuration.Strategy
-import org.apache.spark.mllib.tree.configuration.Algo._
+import org.apache.spark.mllib.tree.configuration.DTParams
import org.apache.spark.mllib.tree.configuration.FeatureType._
-import org.apache.spark.mllib.tree.configuration.QuantileStrategy._
-import org.apache.spark.mllib.tree.impurity.Impurity
+import org.apache.spark.mllib.tree.configuration.QuantileStrategies
+import org.apache.spark.mllib.tree.configuration.QuantileStrategy
import org.apache.spark.mllib.tree.model._
import org.apache.spark.rdd.RDD
import org.apache.spark.util.random.XORShiftRandom
+
/**
* :: Experimental ::
- * A class that implements a decision tree algorithm for classification
and regression. It
- * supports both continuous and categorical features.
- * @param strategy The configuration parameters for the tree algorithm
which specify the type
- * of algorithm (classification, regression, etc.),
feature type (continuous,
- * categorical), depth of the tree, quantile calculation
strategy, etc.
+ * An abstract class for decision tree algorithms for classification and
regression.
+ * It supports both continuous and categorical features.
+ * @param params The configuration parameters for the tree algorithm.
*/
@Experimental
-class DecisionTree (private val strategy: Strategy) extends Serializable
with Logging {
+private[mllib] abstract class DecisionTree[M <: DecisionTreeModel]
(params: DTParams)
--- End diff --
Couldn't find usage for the generic parameter ```M```.
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