sklearn.learning_curve.validation_curve()

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.

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.

For integer/None inputs, if the estimator is a classifier and y is either binary or multiclass, sklearn.model_selection.StratifiedKFold is used. In all other cases, sklearn.model_selection.KFold is used.

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

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 : integer, optional

Number of jobs to run in parallel (default 1).

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.