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[MRG] Fixes model_selection.cross_validate docstring examples #9534

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Aug 12, 2017
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[MRG] Fixes model_selection.cross_validate docstring examples #9534

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10 changes: 6 additions & 4 deletions sklearn/model_selection/_validation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -144,7 +144,7 @@ def cross_validate(estimator, X, y=None, groups=None, scoring=None, cv=None,
Examples
--------
>>> from sklearn import datasets, linear_model
>>> from sklearn.model_selection import cross_val_score
>>> from sklearn.model_selection import cross_validate
>>> from sklearn.metrics.scorer import make_scorer
>>> from sklearn.metrics import confusion_matrix
>>> from sklearn.svm import LinearSVC
Expand All @@ -153,15 +153,17 @@ def cross_validate(estimator, X, y=None, groups=None, scoring=None, cv=None,
>>> y = diabetes.target[:150]
>>> lasso = linear_model.Lasso()

# single metric evaluation using cross_validate
Single metric evaluation using ``cross_validate``

>>> cv_results = cross_validate(lasso, X, y, return_train_score=False)
>>> sorted(cv_results.keys()) # doctest: +ELLIPSIS
['fit_time', 'score_time', 'test_score']
>>> cv_results['test_score'] # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
array([ 0.33..., 0.08..., 0.03...])

# Multiple metric evaluation using cross_validate
# (Please refer the ``scoring`` parameter doc for more information)
Multiple metric evaluation using ``cross_validate``
(please refer the ``scoring`` parameter doc for more information)

>>> scores = cross_validate(lasso, X, y,
... scoring=('r2', 'neg_mean_squared_error'))
>>> print(scores['test_neg_mean_squared_error']) # doctest: +ELLIPSIS
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