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# `sklearn.model_selection`.cross_val_score

sklearn.model_selection.cross_val_score(estimator, X, y=None, *, groups=None, scoring=None, cv=None, n_jobs=None, verbose=0, fit_params=None, pre_dispatch='2*n_jobs', error_score=nan)

  Evaluate a score by cross-validation.

  Read more in the User Guide.

  Parameters:
    estimator estimator object implementing ‘fit’

      The object to use to fit the data.

    X array-like of shape (n_samples, n_features)

      The data to fit. Can be for example a list, or an array.

    y array-like of shape (n_samples,) or (n_samples, n_outputs), default=None

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

    groups array-like of shape (n_samples,), default=None

      Group labels for the samples used while splitting the dataset into train/test set. Only used in conjunction with a “Group” cv instance (e.g., `GroupKFold`).

    scoring str or callable, default=None

      A str (see model evaluation documentation) or a scorer callable object / function with signature `scorer(estimator, X, y)` which should return only a single value.

      Similar to `cross_validate` but only a single metric is permitted.

      If `None`, the estimator’s default scorer (if available) is used.

    cv int, cross-validation generator or an iterable, default=None

      Determines the cross-validation splitting strategy. Possible inputs for cv are:

      - `None`, to use the default 5-fold cross validation,

      - int, to specify the number of folds in a `(Stratified)KFold`,

      - CV splitter,

      - An iterable that generates (train, test) splits as arrays of indices.

      For `int`/`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. These splitters are instantiated with `shuffle=False` so the splits will be the same across calls.

      Refer User Guide for the various cross-validation strategies that can be used here.

      Changed in version 0.22: `cv` default value if `None` changed from 3-fold to 5-fold.

    n_jobs int, default=None

      Number of jobs to run in parallel. Training the estimator and computing the score are parallelized over the cross-validation splits. `None` means 1 unless in a `joblib.parallel_backend` context. `-1` means using all processors. See Glossary for more details.

    verbose int, default=0

      The verbosity level.

    fit_params dict, default=None

      Parameters to pass to the fit method of the estimator.

    pre_dispatch int or str, default=’2*n_jobs’

      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 str, giving an expression as a function of n_jobs, as in ‘2*n_jobs’

    error_score‘raise’ or numeric, default=np.nan

      Value to assign to the score if an error occurs in estimator fitting. If set to ‘raise’, the error is raised. If a numeric value is given, FitFailedWarning is raised.

      New in version 0.20.

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

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

  See also

  `cross_validate`

    To run cross-validation on multiple metrics and also to return train scores, fit times and score times.

  `cross_val_predict`

    Get predictions from each split of cross-validation for diagnostic purposes.

  `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, cv=3))
      [0.3315057  0.08022103 0.03531816]

## Examples using `sklearn.model_selection.cross_val_score`

Release Highlights for scikit-learn 1.3

Release Highlights for scikit-learn 1.3

Model selection with Probabilistic PCA and Factor Analysis (FA)

Model selection with Probabilistic PCA and Factor Analysis (FA)

Imputing missing values before building an estimator

Imputing missing values before building an estimator

Imputing missing values with variants of IterativeImputer

Imputing missing values with variants of IterativeImputer

Nested versus non-nested cross-validation

Nested versus non-nested cross-validation

Plotting Cross-Validated Predictions

Plotting Cross-Validated Predictions

Underfitting vs. Overfitting

Underfitting vs. Overfitting

SVM-Anova: SVM with univariate feature selection

SVM-Anova: SVM with univariate feature selection

Cross-validation on Digits Dataset Exercise

Cross-validation on Digits Dataset Exercise