Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/1798#discussion_r15898657
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala ---
@@ -300,6 +309,93 @@ object DecisionTree extends Serializable with Logging {
new DecisionTree(strategy).train(input)
}
+ /**
+ * Method to train a decision tree model for binary or multiclass
classification.
+ *
+ * @param input Training dataset: RDD of
[[org.apache.spark.mllib.regression.LabeledPoint]].
+ * Labels should take values {0, 1, ..., numClasses-1}.
+ * @param numClassesForClassification number of classes for
classification.
+ * @param categoricalFeaturesInfo Map storing arity of categorical
features.
+ * E.g., an entry (n -> k) indicates that
feature n is categorical
+ * with k categories indexed from 0: {0,
1, ..., k-1}.
+ * @param impurity Criterion used for information gain calculation.
+ * Supported values: "gini" (recommended) or "entropy".
+ * @param maxDepth Maximum depth of the tree.
+ * E.g., depth 0 means 1 leaf node; depth 1 means 1
internal node + 2 leaf nodes.
+ * (suggested value: 4)
+ * @param maxBins maximum number of bins used for splitting features
+ * (suggested value: 100)
+ * @return DecisionTreeModel that can be used for prediction
+ */
+ def trainClassifier(
+ input: RDD[LabeledPoint],
+ numClassesForClassification: Int,
+ categoricalFeaturesInfo: Map[Int, Int],
+ impurity: String,
+ maxDepth: Int,
+ maxBins: Int): DecisionTreeModel = {
+ val impurityType = Impurities.fromString(impurity)
+ train(input, Classification, impurityType, maxDepth,
numClassesForClassification, maxBins, Sort,
+ categoricalFeaturesInfo)
+ }
+
+ /**
+ * Java-friendly API for
[[org.apache.spark.mllib.tree.DecisionTree$#trainClassifier]]
+ */
+ def trainClassifier(
+ input: RDD[LabeledPoint],
--- End diff --
`RDD` -> `JavaRDD`. Sorry I missed this in the first pass.
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