org.apache.spark.mllib.tree.loss

SquaredError

object SquaredError extends Loss

:: DeveloperApi :: Class for squared error loss calculation.

The squared (L2) error is defined as: (y - F(x))**2 where y is the label and F(x) is the model prediction for features x.

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@DeveloperApi()
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Loss, Serializable, Serializable, AnyRef, Any
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  8. def computeError(model: TreeEnsembleModel, data: RDD[LabeledPoint]): Double

    Method to calculate loss of the base learner for the gradient boosting calculation.

    Method to calculate loss 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

    Ensemble model

    data

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

    returns

    Mean squared error of model on data

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

    Method to calculate the gradients for the gradient boosting calculation for least squares error calculation.

    Method to calculate the gradients for the gradient boosting calculation for least squares error calculation. The gradient with respect to F(x) is: - 2 (y - F(x))

    model

    Ensemble model

    point

    Instance of the training dataset

    returns

    Loss gradient

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