Object

ml.combust.mleap.core.tree.loss

LogLoss

Related Doc: package loss

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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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ClassificationLoss, Loss, Serializable, Serializable, AnyRef, Any
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  6. def computeError(prediction: Double, label: Double): Double

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    Method to calculate loss when the predictions are already known.

    Method to calculate loss when the predictions are already known.

    prediction

    Predicted label.

    label

    True label.

    returns

    Measure of model error on datapoint.

    Definition Classes
    LogLoss → Loss
    Note

    This method is used in the method evaluateEachIteration to avoid recomputing the predicted values from previously fit trees.

  7. def computeProbability(margin: Double): Double

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    Returns the estimated probability of a label of 1.0.

    Returns the estimated probability of a label of 1.0.

    Definition Classes
    LogLoss → ClassificationLoss

  8. final def eq(arg0: AnyRef): Boolean

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  11. final def getClass(): Class[_]

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  12. 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

  13. def hashCode(): Int

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Inherited from ClassificationLoss

Inherited from Loss

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

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