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sklearn.cross_validation.cross_val_score

sklearn.cross_validation.cross_val_score(estimator, X, y=None, scoring=None, cv=None, n_jobs=1, verbose=0, fit_params=None, score_func=None, pre_dispatch='2*n_jobs')

Evaluate a score by cross-validation

Parameters:

estimator : estimator object implementing ‘fit’

The object to use to fit the data.

X : array-like

The data to fit. Can be, for example a list, or an array at least 2d.

y : array-like, optional, default: None

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

scoring : string, callable or None, optional, default: None

A string (see model evaluation documentation) or a scorer callable object / function with signature scorer(estimator, X, y).

cv : cross-validation generator, optional, default: None

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.

pre_dispatch : int, or string, optional

Controls the number of jobs that get dispatched during parallel execution. Reducing this number can be useful to avoid an explosion of memory consumption when more jobs get dispatched than CPUs can process. This parameter can be:

  • None, in which case all the jobs are immediately created and spawned. Use this for lightweight and fast-running jobs, to avoid delays due to on-demand spawning of the jobs
  • An int, giving the exact number of total jobs that are spawned
  • A string, giving an expression as a function of n_jobs, as in ‘2*n_jobs’
Returns:

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

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

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