How to Use the AUC ROC Curve for the Multi-class Model?

Kayley Marshall
Kayley MarshallAnswered

It is common practice to measure the efficacy of binary classification models using the Area Under the Curve (AUC) of the ROC curve. Nonetheless, the AUC ROC curve may be employed with minor tweaks when dealing with multi-class classification issues. An example of the AUC ROC curve’s application to a multi-class model is shown below.

OvA approach

The AUC ROC curve for multi-class models is most often used in a One-vs-All (OvA) setting. With this method, you may build an overall AUC ROC curve by treating all classes as positive. Training a set of independent binary classifiers where one class is positive and the others are negative is one approach. After calculating the area under the receiver operating characteristic curve (AUC ROC) for each binary classifier, the curves are averaged to provide a final AUC ROC curve.

OvO approach

A second method of using the ROC curve multi-class model is the one-on-one (OvO) method. With this method, you’ll train a new binary classifier for every possible combination of categories. When there are three categories to distinguish, for instance, three separate binary classifiers would be developed (class 1 vs. class 2, class 1 vs. class 3, and class 2 vs. class 3). After computing the AUC ROC curve for each binary classifier, the curves are concatenated to produce a final AUC ROC curve.

Macro-averaging and micro-averaging

In addition to the OvA and OvO methods, macro-averaging and micro-averaging may also be used to calculate the AUC multiclass classification. With macro-averaging, you take an average of the AUC ROC curves you calculate for each class. Micro-averaging involves summing up predictions for each class and then calculating an AUC ROC curve for the aggregated forecasts.

Wrap Up

It’s vital to remember that the analysis’s precise aims and the nature of the issue will dictate the methodology used. Each method has advantages and disadvantages, which might affect how the findings are interpreted. Using suitable measurements and procedures, it is essential to verify the outcomes after giving considerable thought to the strategy.

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