cross_val_score scoring parameters types
>>> from sklearn import svm, cross_validation, datasets >>> iris = datasets.load_iris() >>> X, y = iris.data, iris.target >>> model = svm.SVC() >>> cross_validation.cross_val_score(model, X, y, scoring='wrong_choice') Traceback (most recent call last): ValueError: 'wrong_choice' is not a valid scoring value. Valid options are ['accuracy', 'adjusted_rand_score', 'average_precision', 'f1', 'log_loss', 'mean_absolute_error', 'mean_squared_error', 'precision', 'r2', 'recall', 'roc_auc']
