object AbsoluteError extends Loss
Class for absolute error loss calculation (for regression).
The absolute (L1) error is defined as: |y - F(x)| where y is the label and F(x) is the model prediction for features x.
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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.
- returns
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 gradients for the gradient boosting calculation for least absolute error calculation.
Method to calculate the gradients for the gradient boosting calculation for least absolute error calculation. The gradient with respect to F(x) is: sign(F(x) - y)
- prediction
Predicted label.
- label
True label.
- returns
Loss gradient
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