mlr_measures_classif.mauc_aunu: Multiclass AUC Scores

In mlr3: Machine Learning in R - Next Generation

Multiclass AUC Scores

Description

Measure to compare true observed labels with predicted probabilities in multiclass classification tasks.

Details

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

This Measure can be instantiated via the dictionary mlr_measures or with the associated sugar function msr():

mlr_measures$get("classif.mauc_aunu")
msr("classif.mauc_aunu")

Parameters

Empty ParamSet

Meta Information

• Type: "classif"

• Range: [0, 1]

• Minimize: FALSE

• Required prediction: prob

Note

The score function calls mlr3measures::mauc_aunu() from package mlr3measures.

If the measure is undefined for the input, NaN is returned. This can be customized by setting the field na_value.

See Also

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_au1u, mlr_measures_classif.mauc_aunp, 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_au1u, mlr_measures_classif.mauc_aunp, mlr_measures_classif.mauc_mu, mlr_measures_classif.mbrier, mlr_measures_classif.mcc

mlr3 documentation built on Oct. 18, 2024, 5:11 p.m.