org.apache.spark.mllib.tree.loss

Class SquaredError

Object

org.apache.spark.mllib.tree.loss.SquaredError

All Implemented Interfaces:

java.io.Serializable, Loss

public class SquaredError
extends Object
implements 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.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
SquaredError()

Method Summary

Methods

Modifier and Type	Method and Description
static double	computeError(TreeEnsembleModel model, RDD<LabeledPoint> data) Method to calculate loss of the base learner for the gradient boosting calculation.
static double	gradient(TreeEnsembleModel model, LabeledPoint point) Method to calculate the gradients for the gradient boosting calculation for least squares error calculation.

Methods inherited from class Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface org.apache.spark.mllib.tree.loss.Loss

computeError, gradient

Constructor Detail

SquaredError

public SquaredError()

Method Detail

gradient

public static double gradient(TreeEnsembleModel model,
              LabeledPoint point)

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))

Parameters:

model - Ensemble model

point - Instance of the training dataset

Returns:

Loss gradient

computeError

public static double computeError(TreeEnsembleModel model,
                  RDD<LabeledPoint> data)

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.

Parameters:

model - Ensemble model

data - Training dataset: RDD of LabeledPoint.

Returns:

Mean squared error of model on data