8.3.11. sklearn.cross_validation.cross_val_score

sklearn.cross_validation.cross_val_score(estimator, X, y=None, score_func=None, cv=None, n_jobs=1, verbose=0, fit_params=None)

Evaluate a score by cross-validation

Parameters:

estimator : estimator object implementing ‘fit’

The object to use to fit the data.

X : array-like of shape at least 2D

The data to fit.

y : array-like, optional

The target variable to try to predict in the case of supervised learning.

score_func : callable, optional

Score function to use for evaluation. Has priority over the score function in the estimator. In a non-supervised setting, where y is None, it takes the test data (X_test) as its only argument. In a supervised setting it takes the test target (y_true) and the test prediction (y_pred) as arguments.

cv : cross-validation generator, optional

A cross-validation generator. If None, a 3-fold cross validation is used or 3-fold stratified cross-validation when y is supplied and estimator is a classifier.

n_jobs : integer, optional

The number of CPUs to use to do the computation. -1 means ‘all CPUs’.

verbose : integer, optional

The verbosity level.

fit_params : dict, optional

Parameters to pass to the fit method of the estimator.

Returns:

scores : array of float, shape=(len(list(cv)),)

Array of scores of the estimator for each run of the cross validation.