/Python/ValueError: 'mean_absolute_error' is not a valid scoring value. Use sorted(sklearn.metrics.SCORERS.keys()) to get valid options.

ValueError: 'mean_absolute_error' is not a valid scoring value. Use sorted(sklearn.metrics.SCORERS.keys()) to get valid options.

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']

Source: scikit-learn.org

ValueError: 'mean_absolute_error' is not a valid scoring value. Use sorted(sklearn.metrics.SCORERS.keys()) to get valid options.

ValueError: 'Precision' is not a valid scoring value. Use sorted(sklearn.metrics.SCORERS.keys()) to get valid options.

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