Github user MechCoder commented on a diff in the pull request:
https://github.com/apache/spark/pull/5330#discussion_r27767011
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala ---
@@ -195,17 +195,24 @@ object GradientBoostedTrees extends Logging {
baseLearners(0) = firstTreeModel
baseLearnerWeights(0) = 1.0
val startingModel = new GradientBoostedTreesModel(Regression,
Array(firstTreeModel), Array(1.0))
- logDebug("error of gbt = " + loss.computeError(startingModel, input))
+
+ var predError: RDD[(Double, Double)] = GradientBoostedTreesModel.
+ computeInitialPredictionAndError(input, 1.0, firstTreeModel, loss)
+ logDebug("error of gbt = " + predError.values.mean())
// Note: A model of type regression is used since we require raw
prediction
timer.stop("building tree 0")
- var bestValidateError = if (validate) loss.computeError(startingModel,
validationInput) else 0.0
+ var validatePredError: RDD[(Double, Double)] =
GradientBoostedTreesModel.
+ computeInitialPredictionAndError(validationInput, 1.0,
firstTreeModel, loss)
+ var bestValidateError = if (validate) validatePredError.values.mean()
else 0.0
var bestM = 1
// psuedo-residual for second iteration
- data = input.map(point => LabeledPoint(loss.gradient(startingModel,
point),
- point.features))
+ data = predError.zip(input).map {
+ case ((pred, _), point) => LabeledPoint(loss.gradient(pred,
point.label), point.features)
--- End diff --
Got it. Thanks a lot for the intuitive explanation.
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