org.apache.spark.mllib.tree.loss

LogLoss

object LogLoss extends Loss

:: DeveloperApi :: 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" ) @DeveloperApi()
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  8. 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

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  13. 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)))

    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)))

    prediction

    Predicted label.

    label

    True label.

    returns

    Loss gradient

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    LogLoss → Loss
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