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.

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()

#### Arguments

eps

(numeric(1))
Very small number to set zero-valued predicted probabilities to in order to prevent errors in log(0) and 1/0 calculation.

se

(logical(1))
If TRUE returns the standard error of the measure.

rm_cens

(logical(1))
Deprecated, please use IPCW instead.

IPCW

(logical(1))
If TRUE (default) removes censored observations and weights score with IPC weighting calculated from the survival probability of the censoring distribution at the time of death.

### 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.