plot_classifier_eval#

scikitplot.metrics.plot_classifier_eval(y_true, y_pred, *, labels=None, normalize=None, digits=3, title='train', ax=None, fig=None, figsize=(8, 3), title_fontsize='large', text_fontsize='medium', cmap=None, x_tick_rotation=0, **kwargs)#

Generates various evaluation plots for a classifier, including confusion matrix, precision-recall curve, and ROC curve.

This function provides a comprehensive view of a classifier’s performance through multiple plots, helping in the assessment of its effectiveness and areas for improvement.

Parameters:

  • y_true (array-like, shape (n_samples,)) – Ground truth (correct) target values.

  • y_pred (array-like, shape (n_samples,)) – Predicted target values from the classifier.

  • labels (list of string, optional) – List of labels for the classes. If None, labels are automatically generated based on the class indices.

  • normalize ({'true', 'pred', 'all', None}, optional) – Normalizes the confusion matrix according to the specified mode. Defaults to None. - ‘true’: Normalizes by true (actual) values. - ‘pred’: Normalizes by predicted values. - ‘all’: Normalizes by total values. - None: No normalization.

  • digits (int, optional, default=3) – Number of digits for formatting floating point values in the plots.

  • title (string, optional, default='train') – Title of the generated plot.

  • ax (list of 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). Need two axes like [fig.add_subplot(1, 2, 1), fig.add_subplot(1, 2, 2)]

  • 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).

    Added in version 0.3.9.

  • figsize (tuple of int, optional, default=(8, 3)) – Tuple denoting figure size of the plot, e.g. (8, 3).

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

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

  • cmap (None, str or matplotlib.colors.Colormap, optional, default=None) – Colormap used for plotting. Options include ‘viridis’, ‘PiYG’, ‘plasma’, ‘inferno’, etc. See Matplotlib Colormap documentation for available choices. - https://matplotlib.org/stable/users/explain/colors/index.html

  • x_tick_rotation (int, optional, default=0) –

    Rotates x-axis tick labels by the specified angle.

    Added in version 0.3.9.

Returns:

fig – The figure on which the plot was drawn.

Return type:

matplotlib.figure.Figure

Notes

The function generates and displays multiple evaluation plots. Ensure that y_true and y_pred have the same shape and contain valid class labels. The normalize parameter is applicable to the confusion matrix plot. Adjust cmap and x_tick_rotation to customize the appearance of the plots.

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.metrics.plot_classifier_eval(
>>>     y_val, y_val_pred,
>>>     title='val',
>>> );

(Source code, png)

Confusion Matrix
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