validation_curve(estimator, X, y, param_name, param_range, groups=None, cv=None, scoring=None, n_jobs=None, pre_dispatch='all', verbose=0, error_score=nan)¶
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.
- estimatorobject type that implements the “fit” and “predict” methods
An object of that type which is cloned for each validation.
- Xarray-like, shape (n_samples, n_features)
Training vector, where n_samples is the number of samples and n_features is the number of features.
- yarray-like, shape (n_samples) or (n_samples, n_features), optional
Target relative to X for classification or regression; None for unsupervised learning.
Name of the parameter that will be varied.
- param_rangearray-like, shape (n_values,)
The values of the parameter that will be evaluated.
- groupsarray-like, with shape (n_samples,), optional
- cvint, cross-validation generator or an iterable, optional
Determines the cross-validation splitting strategy. Possible inputs for cv are:
None, to use the default 5-fold cross validation,
integer, to specify the number of folds in a
An iterable yielding (train, test) splits as arrays of indices.
Refer User Guide for the various cross-validation strategies that can be used here.
Changed in version 0.22:
cvdefault value if None changed from 3-fold to 5-fold.
- scoringstring, 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_jobsint or None, optional (default=None)
- pre_dispatchinteger 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’.
- verboseinteger, optional
Controls the verbosity: the higher, the more messages.
- error_score‘raise’ or numeric
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. This parameter does not affect the refit step, which will always raise the error.
- train_scoresarray, shape (n_ticks, n_cv_folds)
Scores on training sets.
- test_scoresarray, shape (n_ticks, n_cv_folds)
Scores on test set.