Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/2435#discussion_r18014923
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/tree/RandomForest.scala ---
@@ -0,0 +1,430 @@
+/*
+ * 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 scala.collection.JavaConverters._
+import scala.collection.mutable
+
+import org.apache.spark.Logging
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.api.java.JavaRDD
+import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.mllib.tree.configuration.Algo._
+import org.apache.spark.mllib.tree.configuration.QuantileStrategy._
+import org.apache.spark.mllib.tree.configuration.Strategy
+import org.apache.spark.mllib.tree.impl.{BaggedPoint, TreePoint,
DecisionTreeMetadata, TimeTracker}
+import org.apache.spark.mllib.tree.impurity.Impurities
+import org.apache.spark.mllib.tree.model._
+import org.apache.spark.rdd.RDD
+import org.apache.spark.storage.StorageLevel
+import org.apache.spark.util.Utils
+
+/**
+ * :: Experimental ::
+ * A class which implements a random forest learning algorithm for
classification and regression.
+ * It supports both continuous and categorical features.
+ *
+ * @param strategy The configuration parameters for the random forest
algorithm which specify
+ * the type of algorithm (classification, regression,
etc.), feature type
+ * (continuous, categorical), depth of the tree, quantile
calculation strategy,
+ * etc.
+ * @param numTrees If 1, then no bootstrapping is used. If > 1, then
bootstrapping is done.
+ * @param featureSubsetStrategy Number of features to consider for splits
at each node.
+ * Supported: "auto" (default), "all",
"sqrt", "log2", "onethird".
+ * If "auto" is set, this parameter is set
based on numTrees:
+ * if numTrees == 1, then
featureSubsetStrategy = "all";
+ * if numTrees > 1, then
featureSubsetStrategy = "sqrt".
+ * @param seed Random seed for bootstrapping and choosing feature subsets.
+ */
+@Experimental
+private class RandomForest (
+ private val strategy: Strategy,
+ private val numTrees: Int,
+ featureSubsetStrategy: String,
+ private val seed: Int)
--- End diff --
(See comment below)
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