results = cross_val_score(classifier, X, y, cv=k_fold, scoring="roc_auc")
mean_score = results.mean()
print("AUC: ", mean_score)
Now, we are using the cross_val_score() function to evaluate the performance of the model. Here, scoring=“roc_auc” specifies that we are using the AUC score. The output of the above program will be:
AUC: 0.8237768655533179








































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