Calculates the cross-entropy, or logarithmic (log), loss.
The logloss, in the context of probabilistic predictions, is defined as the negative log probability density function, \(f\), evaluated at the observation time, \(t\), $$L(f, t) = -log(f(t))$$
The standard error of the Logloss, 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 IPCW log loss is defined by $$L(f, t, \Delta) = -\Delta log(f(t))/G(t)$$ where \(\Delta\) is the censoring indicator and G is the Kaplan-Meier estimator of the censoring distribution.
Details
If task and train_set are passed to $score then G is fit on training data,
otherwise testing data. The first is likely to reduce any bias caused by calculating
parts of the measure on the test data it is evaluating. The training data is automatically
used in scoring resamplings.
Dictionary
This Measure can be instantiated via the dictionary mlr_measures or with the associated sugar function msr():
MeasureSurvLogloss$new()
mlr_measures$get("surv.logloss")
msr("surv.logloss")See also
Other survival measures:
mlr_measures_surv.calib_alpha,
mlr_measures_surv.calib_beta,
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.dcalib,
mlr_measures_surv.graf,
mlr_measures_surv.intlogloss,
mlr_measures_surv.rcll,
mlr_measures_surv.schmid
Super classes
mlr3::Measure -> mlr3proba::MeasureSurv -> MeasureSurvLogloss