mlr_measures_regr.logloss: Log Loss Regression Measure

In mlr-org/mlr3proba: Probabilistic Supervised Learning for 'mlr3'

Log Loss Regression Measure

Description

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.

mlr-org/mlr3proba documentation built on April 12, 2025, 4:38 p.m.