R bloggers

Probabilistic interpretation of AUC

FavoriteLoadingAdd to favorites

Unfortunately this was not taught in any of my statistics or data analysis classes at university (wtf it so needs to be :scream_cat:).So it took me some until I learned that the AUC has a nice probabilistic meaning. What’s AUC anyway? Consider: A dataset : , where A classification algorithm (like logistic regression, SVM, deep neural net, or whatever you like), trained on , that assigns a score (or probability) to any new observation signifying how likely its label is . Then: A decision threshold (or operating point) can be chosen to assign a class label ( or ) to based on the value of .The chosen threshold determines the balance between how many false positives and false negatives will result from this classification. Plotting the true positive rate (TPR) against the false positive rate (FPR) as the operating point…
Original Post: Probabilistic interpretation of AUC