Package

org.apache.spark.mllib.tree

loss

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package loss

Visibility
  1. Public
  2. All

Type Members

  1. trait Loss extends Serializable

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    :: DeveloperApi :: Trait for adding "pluggable" loss functions for the gradient boosting algorithm.

    :: DeveloperApi :: Trait for adding "pluggable" loss functions for the gradient boosting algorithm.

    Annotations
    @Since( "1.2.0" ) @DeveloperApi()

Value Members

  1. object AbsoluteError extends Loss

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    :: DeveloperApi :: Class for absolute error loss calculation (for regression).

    :: DeveloperApi :: Class for absolute error loss calculation (for regression).

    The absolute (L1) error is defined as: |y - F(x)| where y is the label and F(x) is the model prediction for features x.

    Annotations
    @Since( "1.2.0" ) @DeveloperApi()
  2. object LogLoss extends Loss

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    :: DeveloperApi :: Class for log loss calculation (for classification).

    :: 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.

    Annotations
    @Since( "1.2.0" ) @DeveloperApi()
  3. object Losses

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    Annotations
    @Since( "1.2.0" )
  4. object SquaredError extends Loss

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

    :: 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.

    Annotations
    @Since( "1.2.0" ) @DeveloperApi()

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