Log Loss Regression Measure

Calculates the cross-entropy, or logarithmic (log), loss.

Details

The Log Loss, in the context of probabilistic predictions, is defined as the negative log probability density function, \(f\), evaluated at the observed value, \(y\), $$L(f, y) = -\log(f(y))$$

Parameters

Id	Type	Default	Range
eps	numeric	1e-15	\([0, 1]\)

Meta Information

• Type: "regr"

• Range: \([0, \infty)\)

• Minimize: TRUE

• Required prediction: distr

Parameter details

• eps (numeric(1))
  Very small number to substitute zero values in order to prevent errors in e.g. log(0) and/or division-by-zero calculations. Default value is 1e-15.

Super classes

mlr3::Measure -> mlr3::MeasureRegr -> MeasureRegrLogloss

Methods

Public methods

• MeasureRegrLogloss$new()

• MeasureRegrLogloss$clone()

Inherited methods

• mlr3::Measure$aggregate()
• mlr3::Measure$format()
• mlr3::Measure$help()
• mlr3::Measure$print()
• mlr3::Measure$score()

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Method new()

Creates a new instance of this R6 class.

Usage

MeasureRegrLogloss$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

MeasureRegrLogloss$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.