sklearn.model_selection.validation_curve

sklearn.model_selection.validation_curve(estimator, X, y, param_name, param_range, groups=None, cv='warn', scoring=None, n_jobs=None, pre_dispatch='all', verbose=0, error_score='raise-deprecating')

Validation curve.

Determine training and test scores for varying parameter values.

Compute scores for an estimator with different values of a specified parameter. This is similar to grid search with one parameter. However, this will also compute training scores and is merely a utility for plotting the results.

Read more in the User Guide.

Parameters:

estimator : object type that implements the “fit” and “predict” methods

An object of that type which is cloned for each validation.

X : array-like, shape (n_samples, n_features)

Training vector, where n_samples is the number of samples and n_features is the number of features.

y : array-like, shape (n_samples) or (n_samples, n_features), optional

Target relative to X for classification or regression; None for unsupervised learning.

param_name : string

Name of the parameter that will be varied.

param_range : array-like, shape (n_values,)

The values of the parameter that will be evaluated.

groups : array-like, with shape (n_samples,), optional

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).

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,
• CV splitter,
• An iterable yielding (train, test) splits as arrays of indices.

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.

Changed in version 0.20: cv default value if None will change from 3-fold to 5-fold in v0.22.

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).

n_jobs : int or None, optional (default=None)

Number of jobs to run in parallel. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.

pre_dispatch : integer or string, optional

Number of predispatched jobs for parallel execution (default is all). The option can reduce the allocated memory. The string can be an expression like ‘2*n_jobs’.

verbose : integer, optional

Controls the verbosity: the higher, the more messages.

error_score : ‘raise’ | ‘raise-deprecating’ or numeric

Value to assign to the score if an error occurs in estimator fitting. If set to ‘raise’, the error is raised. If set to ‘raise-deprecating’, a FutureWarning is printed before the error is raised. If a numeric value is given, FitFailedWarning is raised. This parameter does not affect the refit step, which will always raise the error. Default is ‘raise-deprecating’ but from version 0.22 it will change to np.nan.

Returns:

train_scores : array, shape (n_ticks, n_cv_folds)

Scores on training sets.

test_scores : array, shape (n_ticks, n_cv_folds)

Scores on test set.

Notes

See Plotting Validation Curves

Examples using sklearn.model_selection.validation_curve

Plotting Validation Curves