[GitHub] spark pull request: MLI-1 Decision Trees

[mengxr]

Github user mengxr commented on a diff in the pull request:

    https://github.com/apache/spark/pull/79#discussion_r10442463
  
    --- Diff: 
mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala 
---
    @@ -0,0 +1,58 @@
    +/*
    + * 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.model
    +
    +import org.apache.spark.mllib.tree.configuration.Algo._
    +import org.apache.spark.rdd.RDD
    +
    +/**
    + * Model to store the decision tree parameters
    + * @param topNode root node
    + * @param algo algorithm type -- classification or regression
    + */
    +class DecisionTreeModel(val topNode: Node, val algo: Algo) extends 
Serializable {
    +
    +  /**
    +   * Predict values for a single data point using the model trained.
    +   *
    +   * @param features array representing a single data point
    +   * @return Double prediction from the trained model
    +   */
    +  def predict(features: Array[Double]): Double = {
    +    algo match {
    +      case Classification => {
    +        if (topNode.predictIfLeaf(features) < 0.5) 0.0 else 1.0
    --- End diff --
    
    Same question was asked by @srowen : It is easy to support multi-class?
    
    Also, why 0.5 is used here as the threshold?


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