org.apache.spark.mllib.tree.loss

Loss

trait Loss extends Serializable

:: DeveloperApi :: Trait for adding "pluggable" loss functions for the gradient boosting algorithm.

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@DeveloperApi()
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AbsoluteError, LogLoss, SquaredError
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  1. abstract 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. Note: This method is not used by the gradient boosting algorithm but is useful for debugging purposes.

    model

    Model of the weak learner.

    data

    Training dataset: RDD of org.apache.spark.mllib.regression.LabeledPoint.

    returns

    Measure of model error on data

  2. abstract def gradient(model: TreeEnsembleModel, point: LabeledPoint): Double

    Method to calculate the gradients for the gradient boosting calculation.

    Method to calculate the gradients for the gradient boosting calculation.

    model

    Model of the weak learner.

    point

    Instance of the training dataset.

    returns

    Loss gradient.

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