Object

org.apache.spark.mllib.tree.loss

LogLoss

Related Doc: package loss

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object LogLoss extends ClassificationLoss

:: 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()
Source
LogLoss.scala
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ClassificationLoss, Loss, Serializable, Serializable, AnyRef, Any
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  1. LogLoss
  2. ClassificationLoss
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  6. def computeError(model: TreeEnsembleModel, data: RDD[LabeledPoint]): Double

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    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

    Definition Classes
    Loss
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    @Since( "1.2.0" )
    Note

    This method is not used by the gradient boosting algorithm but is useful for debugging purposes.

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  11. def gradient(prediction: Double, label: Double): Double

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    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

    Definition Classes
    LogLoss → Loss
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    @Since( "1.2.0" )

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Inherited from ClassificationLoss

Inherited from Loss

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