plot_learning_curve#

scikitplot.estimators.plot_learning_curve(estimator, X, y, *, train_sizes=None, cv=None, scoring=None, n_jobs=None, verbose=0, shuffle=False, random_state=None, fit_params=None, title='Learning Curves', ax=None, fig=None, figsize=None, title_fontsize='large', text_fontsize='medium', **kwargs)#

Generates a plot of the train and test learning curves for a classifier.

The learning curves plot the performance of a classifier as a function of the number of training samples. This helps in understanding how well the classifier performs with different amounts of training data.

Parameters:

  • estimator (object type that implements the "fit" method) – An object of that type which is cloned for each validation. It must also implement “predict” unless scoring is a callable that doesn’t rely on “predict” to compute a score.

  • X (array-like, shape (n_samples, n_features)) – Training data, 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.

  • train_sizes (iterable, optional) – Determines the training sizes used to plot the learning curve. If None, np.linspace(.1, 1.0, 5) is used.

  • cv (int, cross-validation generator, iterable or None, default=5) –

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

    For integer/None inputs, if classifier is True 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.

  • scoring (str, callable, or None, optional, default=None) – A string (see scikit-learn model evaluation documentation) or a scorer callable object/function with signature scorer(estimator, X, y).

  • n_jobs (int, optional, default=None) – Number of jobs to run in parallel. Training the estimator and computing the score are parallelized over the different training and test sets. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.

  • verbose (int, default=0) – Controls the verbosity: the higher, the more messages.

  • shuffle (bool, optional, default=True) – Whether to shuffle the training data before splitting using cross-validation.

  • random_state (int or RandomState, optional) – Pseudo-random number generator state used for random sampling.

  • fit_params (dict, default=None) –

    Parameters to pass to the fit method of the estimator.

    Added in version 0.3.9.

  • title (str, optional, default="Learning Curves") – Title of the generated plot.

  • ax (matplotlib.axes.Axes, optional, default=None) – The axis to plot the figure on. If None is passed in the current axes will be used (or generated if required).

  • fig (matplotlib.pyplot.figure, optional, default: None) – The figure to plot the Visualizer on. If None is passed in the current plot will be used (or generated if required).

  • figsize (tuple of int, optional, default=None) – Tuple denoting figure size of the plot, e.g., (6, 6).

  • title_fontsize (str or int, optional, default='large') – Font size for the plot title. Use e.g., “small”, “medium”, “large” or integer values.

  • text_fontsize (str or int, optional, default='medium') – Font size for the text in the plot. Use e.g., “small”, “medium”, “large” or integer values.

  • kwargs (dict) – generic keyword arguments.

Returns:

The axes on which the plot was drawn.

Return type:

matplotlib.axes.Axes

References#

Examples

>>> from sklearn.datasets import load_digits as data_10_classes
>>> from sklearn.model_selection import train_test_split
>>> from sklearn.naive_bayes import GaussianNB
>>> import scikitplot as skplt
>>> X, y = data_10_classes(return_X_y=True, as_frame=False)
>>> X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.5, random_state=0)
>>> model = GaussianNB()
>>> model.fit(X_train, y_train)
>>> y_val_pred = model.predict(X_val)
>>> skplt.estimators.plot_learning_curve(
>>>     model, X_val, y_val_pred,
>>> );

(Source code, png)

Learning Curves
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