Measure to compare true observed labels with predicted probabilities in multiclass classification tasks.
This Measure can be instantiated via the dictionary mlr_measures or with the associated sugar function msr():
mlr_measures$get("classif.mauc_au1u")
msr("classif.mauc_au1u")
Empty ParamSet
Type: "classif"
Range: \([0, 1]\)
Minimize: FALSE
Required prediction: prob
Multiclass AUC measures.
AUNU: AUC of each class against the rest, using the uniform class
distribution. Computes the AUC treating a c-dimensional classifier
as c two-dimensional 1-vs-rest classifiers, where classes are assumed to have
uniform distribution, in order to have a measure which is independent
of class distribution change (Fawcett 2001).
AUNP: AUC of each class against the rest, using the a-priori class
distribution. Computes the AUC treating a c-dimensional classifier as c
two-dimensional 1-vs-rest classifiers, taking into account the prior probability of
each class (Fawcett 2001).
AU1U: AUC of each class against each other, using the uniform class
distribution. Computes something like the AUC of c(c - 1) binary classifiers
(all possible pairwise combinations). See Hand (2001) for details.
AU1P: AUC of each class against each other, using the a-priori class
distribution. Computes something like AUC of c(c - 1) binary classifiers
while considering the a-priori distribution of the classes as suggested
in Ferri (2009). Note we deviate from the definition in
Ferri (2009) by a factor of c.
MU: Multiclass AUC as defined in Kleinman and Page (2019). This measure is an average of the pairwise AUCs between all classes. The measure was tested against the Python implementation by Ross Kleinman.
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.bbrier,
mlr_measures_classif.ce,
mlr_measures_classif.costs,
mlr_measures_classif.dor,
mlr_measures_classif.fbeta,
mlr_measures_classif.fdr,
mlr_measures_classif.fn,
mlr_measures_classif.fnr,
mlr_measures_classif.fomr,
mlr_measures_classif.fp,
mlr_measures_classif.fpr,
mlr_measures_classif.logloss,
mlr_measures_classif.mauc_au1p,
mlr_measures_classif.mauc_aunp,
mlr_measures_classif.mauc_aunu,
mlr_measures_classif.mauc_mu,
mlr_measures_classif.mbrier,
mlr_measures_classif.mcc,
mlr_measures_classif.npv,
mlr_measures_classif.ppv,
mlr_measures_classif.prauc,
mlr_measures_classif.precision,
mlr_measures_classif.recall,
mlr_measures_classif.sensitivity,
mlr_measures_classif.specificity,
mlr_measures_classif.tn,
mlr_measures_classif.tnr,
mlr_measures_classif.tp,
mlr_measures_classif.tpr
Other multiclass classification measures:
mlr_measures_classif.acc,
mlr_measures_classif.bacc,
mlr_measures_classif.ce,
mlr_measures_classif.costs,
mlr_measures_classif.logloss,
mlr_measures_classif.mauc_au1p,
mlr_measures_classif.mauc_aunp,
mlr_measures_classif.mauc_aunu,
mlr_measures_classif.mauc_mu,
mlr_measures_classif.mbrier,
mlr_measures_classif.mcc