Pour une classe donnée, un classifieur, et un exemple, quatre cas peuvent se présenter :
1. Le classifieur ne se trompe pas : c’est un vrai positif.
2. Le classifieur se trompe : c’est un faux négatif.
3. Le classifieur la lui attribue quand même : c’est faux positif.
4. Le classifieur ne le range pas non plus dans cette classe : c’est un vrai
A.1.20.1 TP Rate
Rapport (ratio) des vrais positifs. Il correspond à :
nbre de vrais positi f s
(nbre de vrais posit f s+nbre de f aux ngati f s) =
nbre de vrais positi f s nbre d0exemples de cette classe
C’est donc le rapport entre le nombre de bien classé et le nombre total d’éléments qui devraient être bien classes.
A.1.20.2 FP Rate
Rapport des faux positifs. Il correspond à :
nbre de f aux positi f s
(nbrede f aux posit f s+nbre de vrais ngati f s) =
nbre de f aux positi f s
nbre d0exemples n0etantpas de cette classe
La donnée des taux TP Rate et FP Rate permet de reconstruire la matrice de confusion pour une classe donnée.
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