原版技术文档链接
方法原型：
sklearn.model_selection.cross_val_score(estimator, X, y=None, groups=None, scoring=None, cv=None, n_jobs=1, verbose=0, fit_params=None, pre_dispatch=‘2*n_jobs’)[source]
该方法就是通过交叉验证模型然后得出一个分数
Read more in the User Guide.
| 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.
y : array-like, optional, default: None
The target variable to try to predict in the case of supervised learning.
groups : array-like, with shape (n_samples,), optional
Group labels for the samples used while splitting the dataset into train/test set.
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 in a (Stratified)KFold,
- An object to be used as a cross-validation generator.
- An iterable yielding train, test splits.
For integer/None inputs, if the estimator is a classifier and y is either binary or multiclass, StratifiedKFold is used. In all other cases, KFold is used.
Refer User Guide for the various cross-validation strategies that can be used here.
一般情况下，我们默认使用KFold，CV参数的值为int类型，对应的就是K折的k值。
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’
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| 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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See also sklearn.model_selection.cross_validate
To run cross-validation on multiple metrics and also to return train scores, fit times and score times. sklearn.metrics.make_scorer
Make a scorer from a performance metric or loss function. Examples >>> >>> from sklearn import datasets, linear_model
>>> from sklearn.model_selection 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]
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See also
sklearn.metrics.make_scorer
Make a scorer from a performance metric or loss function.
例子：
>>>
>>> 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]
