Précision pour chaque classe
from sklearn.metrics import confusion_matrix
import numpy as np
y_true = [0, 1, 2, 2, 2]
y_pred = [0, 0, 2, 2, 1]
target_names = ['class 0', 'class 1', 'class 2']
#Get the confusion matrix
cm = confusion_matrix(y_true, y_pred)
#array([[1, 0, 0],
# [1, 0, 0],
# [0, 1, 2]])
#Now the normalize the diagonal entries
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
#array([[1. , 0. , 0. ],
# [1. , 0. , 0. ],
# [0. , 0.33333333, 0.66666667]])
#The diagonal entries are the accuracies of each class
cm.diagonal()
#array([1. , 0. , 0.66666667])
Real Raccoon