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from sklearn.datasets import load_digits
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digits = load_digits()
X = digits.data
y = digits.target
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from sklearn.cross_validation import cross_val_score
from sklearn.svm import LinearSVC
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cross_val_score(LinearSVC(), X, y)
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cross_val_score(LinearSVC(), X, y, cv=5, scoring="f1_macro")
Let's go to a binary task for a moment (even vs uneven)
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y % 2
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cross_val_score(LinearSVC(), X, y % 2, scoring="average_precision")
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cross_val_score(LinearSVC(), X, y % 2, scoring="roc_auc")
There are other ways to do cross-valiation
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from sklearn.cross_validation import ShuffleSplit
shuffle_split = ShuffleSplit(len(X), 10, test_size=.4)
cross_val_score(LinearSVC(), X, y, cv=shuffle_split)
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