cross_val_score(estimator, X, y=None, scoring=None, cv=None, n_jobs=1, verbose=0, fit_params=None, pre_dispatch='2*n_jobs')¶
Evaluate a score by cross-validation
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 or int, optional, default: None
A cross-validation generator to use. If int, determines the number of folds in StratifiedKFold if y is binary or multiclass and estimator is a classifier, or the number of folds in KFold otherwise. If None, it is equivalent to cv=3.
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’
scores : array of float, shape=(len(list(cv)),)
Array of scores of the estimator for each run of the cross validation.