object LogLoss extends ClassificationLoss
Class for log loss calculation (for classification). This uses twice the binomial negative log likelihood, called "deviance" in Friedman (1999).
The log loss is defined as: 2 log(1 + exp(-2 y F(x))) where y is a label in {-1, 1} and F(x) is the model prediction for features x.
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- @Since( "1.2.0" )
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- LogLoss.scala
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def
computeError(model: TreeEnsembleModel, data: RDD[LabeledPoint]): Double
Method to calculate error of the base learner for the gradient boosting calculation.
Method to calculate error of the base learner for the gradient boosting calculation.
- model
Model of the weak learner.
- data
Training dataset: RDD of org.apache.spark.mllib.regression.LabeledPoint.
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Measure of model error on data
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- Loss
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This method is not used by the gradient boosting algorithm but is useful for debugging purposes.
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def
gradient(prediction: Double, label: Double): Double
Method to calculate the loss gradients for the gradient boosting calculation for binary classification The gradient with respect to F(x) is: - 4 y / (1 + exp(2 y F(x)))
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