x
import numpy as npfrom sklearn.metrics import accuracy_score, confusion_matrix, roc_auc_score, roc_curven = 10000ratio = .95n_0 = int((1-ratio) * n)n_1 = int(ratio * n)y = np.array([0] * n_0 + [1] * n_1)# below are the probabilities obtained from a hypothetical model that doesn't always predict the modey_proba_2 = np.array( np.random.uniform(0, .7, n_0).tolist() + np.random.uniform(.3, 1, n_1).tolist())y_pred_2 = y_proba_2 > .5print(f'accuracy score: {accuracy_score(y, y_pred_2)}')cf_mat = confusion_matrix(y, y_pred_2)print('Confusion matrix')print(cf_mat)print(f'class 0 accuracy: {cf_mat[0][0]/n_0}')print(f'class 1 accuracy: {cf_mat[1][1]/n_1}')accuracy score: 0.719 Confusion matrix [[ 382 118] [2692 6808]] class 0 accuracy: 0.764 class 1 accuracy: 0.7166315789473684