mlr_measures_surv.logloss: Negative Log-Likelihood Survival Measure

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

Negative Log-Likelihood Survival Measure

Description

Calculates the cross-entropy, or negative log-likelihood (NLL) 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 observation time (event or censoring), t,

L_{NLL}(f, t) = -\log[f(t)]

The standard error of the Log Loss, L, is approximated via,

se(L) = sd(L)/\sqrt{N}

where N are the number of observations in the test set, and sd is the standard deviation.

The Re-weighted Negative Log-Likelihood (RNLL) or IPCW (Inverse Probability Censoring Weighted) Log Loss is defined by

L_{RNLL}(f, t, \delta) = - \frac{\delta \log[f(t)]}{G(t)}

where \delta is the censoring indicator and G(t) is the Kaplan-Meier estimator of the censoring distribution. So only observations that have experienced the event are taking into account for RNLL (i.e. \delta = 1) and both f(t), G(t) are calculated only at the event times. If only censored observations exist in the test set, NaN is returned.

Dictionary

This Measure can be instantiated via the dictionary mlr_measures or with the associated sugar function msr():

MeasureSurvLogloss$new()
mlr_measures$get("surv.logloss")
msr("surv.logloss")

Parameters

Id	Type	Default	Levels	Range
eps	numeric	1e-15		[0, 1]
se	logical	FALSE	TRUE, FALSE	-
IPCW	logical	TRUE	TRUE, FALSE	-
ERV	logical	FALSE	TRUE, FALSE	-

Meta Information

• Type: "surv"

• 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.

• se (logical(1))
  If TRUE then returns standard error of the measure otherwise returns the mean across all individual scores, e.g. the mean of the per observation scores. Default is FALSE (returns the mean).

• ERV (logical(1))
  If TRUE then the Explained Residual Variation method is applied, which means the score is standardized against a Kaplan-Meier baseline. Default is FALSE.

• IPCW (logical(1))
  If TRUE (default) then returns the L_{RNLL} score (which is proper), otherwise the L_{NLL} score (improper). See Sonabend et al. (2024) for more details.

Data used for Estimating Censoring Distribution

If task and train_set are passed to ⁠$score⁠ then G(t) is fit using all observations from the train set, otherwise the test set is used. Using the train set is likely to reduce any bias caused by calculating parts of the measure on the test data it is evaluating. Also usually it means that more data is used for fitting the censoring distribution G(t) via the Kaplan-Meier. The training data is automatically used in scoring resamplings.

Super classes

mlr3::Measure -> mlr3proba::MeasureSurv -> MeasureSurvLogloss

Methods

Public methods

• MeasureSurvLogloss$new()

• MeasureSurvLogloss$clone()

Inherited methods

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

---

Method new()

Creates a new instance of this R6 class.

Usage

MeasureSurvLogloss$new(ERV = FALSE)

Arguments

ERV

(logical(1))
Standardize measure against a Kaplan-Meier baseline (Explained Residual Variation)

---

Method clone()

The objects of this class are cloneable with this method.

Usage

MeasureSurvLogloss$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

References

Sonabend, Raphael, Zobolas, John, Kopper, Philipp, Burk, Lukas, Bender, Andreas (2024). “Examining properness in the external validation of survival models with squared and logarithmic losses.” https://arxiv.org/abs/2212.05260v2.

See Also

Other survival measures: mlr_measures_surv.calib_alpha, mlr_measures_surv.calib_beta, mlr_measures_surv.calib_index, mlr_measures_surv.chambless_auc, mlr_measures_surv.cindex, mlr_measures_surv.dcalib, mlr_measures_surv.graf, mlr_measures_surv.hung_auc, mlr_measures_surv.intlogloss, mlr_measures_surv.mae, mlr_measures_surv.mse, mlr_measures_surv.nagelk_r2, mlr_measures_surv.oquigley_r2, mlr_measures_surv.rcll, mlr_measures_surv.rmse, mlr_measures_surv.schmid, mlr_measures_surv.song_auc, mlr_measures_surv.song_tnr, mlr_measures_surv.song_tpr, mlr_measures_surv.uno_auc, mlr_measures_surv.uno_tnr, mlr_measures_surv.uno_tpr, mlr_measures_surv.xu_r2

Other Probabilistic survival measures: mlr_measures_surv.graf, mlr_measures_surv.intlogloss, mlr_measures_surv.rcll, mlr_measures_surv.schmid

Other distr survival measures: mlr_measures_surv.calib_alpha, mlr_measures_surv.calib_index, mlr_measures_surv.dcalib, mlr_measures_surv.graf, mlr_measures_surv.intlogloss, mlr_measures_surv.rcll, mlr_measures_surv.schmid

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