Description
Create a helper method for computing the error at each iteration of boosting. This should be used post-hoc to compute the error efficiently on a new dataset.
E.g.:
def evaluateEachIteration(data: RDD[LabeledPoint], evaluator): Array[Double]
Notes:
- It should run in the same big-O time as predict() by keeping a running total (residual).
- A different method name could be good.
- It could take an evaluator and/or could evaluate using the training metric by default.
Attachments
Issue Links
- is related to
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SPARK-5972 Cache residuals for GradientBoostedTrees during training
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- Resolved
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- links to
