>>> >>> from sklearn import svm, datasets >>> from sklearn.model_selection import cross_val_score >>> iris = datasets.load_iris() >>> X, y = iris.data, iris.target >>> clf = svm.SVC(probability=True, random_state=0) >>> cross_val_score(clf, X, y, scoring='neg_log_loss') array([-0.07..., -0.16..., -0.06...]) >>> model = svm.SVC() >>> cross_val_score(model, X, y, scoring='wrong_choice') Traceback (m
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