Classification measure defined as $$ -\frac{1}{n} \sum_{i=1}^n \log \left( p_i \right ) $$ where \(p_i\) is the probability for the true class of observation \(i\).
R6::R6Class() inheriting from Measure.
This measures can be retrieved from the dictionary mlr_measures:
mlr_measures$get("classif.logloss")
msr("classif.logloss")
Type: "classif"
Range: \([0, \infty)\)
Minimize: TRUE
Required prediction: prob
Dictionary of Measures: mlr_measures
as.data.table(mlr_measures) for a complete table of all (also dynamically created) Measure implementations.
Other classification measures:
mlr_measures_classif.acc,
mlr_measures_classif.auc,
mlr_measures_classif.bacc,
mlr_measures_classif.ce,
mlr_measures_classif.costs,
mlr_measures_classif.dor,
mlr_measures_classif.fbeta,
mlr_measures_classif.fdr,
mlr_measures_classif.fnr,
mlr_measures_classif.fn,
mlr_measures_classif.fomr,
mlr_measures_classif.fpr,
mlr_measures_classif.fp,
mlr_measures_classif.mcc,
mlr_measures_classif.npv,
mlr_measures_classif.ppv,
mlr_measures_classif.precision,
mlr_measures_classif.recall,
mlr_measures_classif.sensitivity,
mlr_measures_classif.specificity,
mlr_measures_classif.tnr,
mlr_measures_classif.tn,
mlr_measures_classif.tpr,
mlr_measures_classif.tp
Other multiclass classification measures:
mlr_measures_classif.acc,
mlr_measures_classif.bacc,
mlr_measures_classif.ce,
mlr_measures_classif.costs