Github user Ishiihara commented on a diff in the pull request:
https://github.com/apache/spark/pull/2394#discussion_r18106962
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/regression/StochasticGradientBoosting.scala
---
@@ -0,0 +1,173 @@
+package org.apache.spark.mllib.regression
+
+import org.apache.spark.mllib.linalg.Vector
+import org.apache.spark.mllib.tree.DecisionTree
+import org.apache.spark.mllib.tree.configuration.Algo.Algo
+import org.apache.spark.mllib.tree.configuration.Strategy
+import org.apache.spark.mllib.tree.impurity.Impurity
+import org.apache.spark.mllib.tree.model.DecisionTreeModel
+import org.apache.spark.rdd.{DoubleRDDFunctions, RDD}
+import scala.util.Random
+
+/**
+ *
+ * Read about the algorithm "Gradient boosting" here:
+ *
http://www.montefiore.ulg.ac.be/services/stochastic/pubs/2007/GWD07/geurts-icml2007.pdf
+ *
+ * Libraries that implement the algorithm "Gradient boosting" similar way
+ * https://code.google.com/p/jforests/
+ * https://code.google.com/p/jsgbm/
+ *
+ */
+class StochasticGradientBoosting {
+
+ /**
+ * Train a Gradient Boosting model given an RDD of (label, features)
pairs.
+ *
+ * @param input Training dataset: RDD of
[[org.apache.spark.mllib.regression.LabeledPoint]].
+ * @param leaningRate Learning rate
+ * @param countOfTrees Number of trees.
+ * @param samplingSizeRatio Size of random sample, percent of ${input}
size.
+ * @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.
+ * @return StochasticGradientBoostingModel that can be used for
prediction
+ */
+ def run(
+ input : RDD[LabeledPoint],
+ leaningRate : Double,
+ countOfTrees : Int,
+ samplingSizeRatio : Double,
+ strategy: Strategy): StochasticGradientBoostingModel = {
+
+ val featureDimension = input.count()
--- End diff --
This is not feature dimension, rather, input.count() is the number of
LabeledPoint in your RDD. if you would like to compute feature dimension,
please use
input.take(1).features.length
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