Calculates the right-censored logarithmic (log), loss.
The RCLL, in the context of probabilistic predictions, is defined by $$L(f, t, \Delta) = -log(\Delta f(t) + (1 - \Delta) S(t))$$ where \(\Delta\) is the censoring indicator.
This Measure can be instantiated via the dictionary mlr_measures or with the associated sugar function msr():
MeasureSurvRCLL$new() mlr_measures$get("surv.rcll") msr("surv.rcll")
Type: "surv"
Range: \((-\infty, \infty)\)
Minimize: TRUE
Required prediction: distr
mlr3::Measure
-> mlr3proba::MeasureSurv
-> MeasureSurvRCLL
new()
Creates a new instance of this R6 class.
MeasureSurvRCLL$new()
clone()
The objects of this class are cloneable with this method.
MeasureSurvRCLL$clone(deep = FALSE)
deep
Whether to make a deep clone.
Avati, A., Duan, T., Zhou, S., Jung, K., Shah, N. H., & Ng, A. (2018). Countdown Regression: Sharp and Calibrated Survival Predictions. http://arxiv.org/abs/1806.08324
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.logloss
,
mlr_measures_surv.mae
,
mlr_measures_surv.mse
,
mlr_measures_surv.nagelk_r2
,
mlr_measures_surv.oquigley_r2
,
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.logloss
,
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.logloss
,
mlr_measures_surv.schmid