Cut Off, Lift, Binary Classification, Model Performance, Performance Metrics.
roc_curve(true_y, y_prob) plt.plot(fpr, tpr) plt.xlabel('False Positive Rate') plt.ylabel('True Positive Rate') print(f'AUC score: {roc_auc_score(df.CHURN_TARGET, df.MODEL_SCORE)}') plot_roc_curve(df.CHURN_TARGET, df.MODEL_SCORE) Output With an AUC value of 0.98, we can interpret it as the