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
Deprecated since version 0.18: This module will be removed in 0.20. Use
Read more in the User Guide.
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 : int, cross-validation generator or an iterable, optional
Determines the cross-validation splitting strategy. Possible inputs for cv are:
- None, to use the default 3-fold cross-validation,
- integer, to specify the number of folds.
- An object to be used as a cross-validation generator.
- An iterable yielding train/test splits.
Refer User Guide for the various cross-validation strategies that can be used here.
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.
- Make a scorer from a performance metric or loss function.
>>> from sklearn import datasets, linear_model >>> from sklearn.cross_validation import cross_val_score >>> diabetes = datasets.load_diabetes() >>> X = diabetes.data[:150] >>> y = diabetes.target[:150] >>> lasso = linear_model.Lasso() >>> print(cross_val_score(lasso, X, y)) [ 0.33150734 0.08022311 0.03531764]