Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/79#discussion_r10439706
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala ---
@@ -0,0 +1,1055 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.mllib.tree
+
+import org.apache.spark.SparkContext._
+import org.apache.spark.rdd.RDD
+import org.apache.spark.mllib.tree.model._
+import org.apache.spark.{SparkContext, Logging}
+import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.mllib.tree.model.Split
+import org.apache.spark.mllib.tree.configuration.Strategy
+import org.apache.spark.mllib.tree.configuration.QuantileStrategy._
+import org.apache.spark.mllib.tree.configuration.FeatureType._
+import org.apache.spark.mllib.tree.configuration.Algo._
+import org.apache.spark.mllib.tree.impurity.{Variance, Entropy, Gini,
Impurity}
+import scala.util.control.Breaks._
+
+/**
+ * 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.
+ */
+class DecisionTree private(val strategy: Strategy) extends Serializable
with Logging {
+
+ /**
+ * Method to train a decision tree model over an RDD
+ * @param input RDD of
[[org.apache.spark.mllib.regression.LabeledPoint]] used as training data
+ * for DecisionTree
+ * @return a DecisionTreeModel that can be used for prediction
+ */
+ def train(input: RDD[LabeledPoint]): DecisionTreeModel = {
+
+ //Cache input RDD for speedup during multiple passes
+ input.cache()
--- End diff --
In the current implementation of other algorithms in MLlib, we let users to
choose whether the data should be cached or not. How many passes does your
algorithm need?
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