plot_classifier_eval#

scikitplot.api.metrics.plot_classifier_eval(y_true, y_pred, *, labels=None, normalize=None, digits=3, title='train', title_fontsize='large', text_fontsize='medium', cmap=None, x_tick_rotation=0, figsize=(8, 3), nrows=1, ncols=2, index=2, **kwargs)[source]#

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_truearray-like, shape (n_samples,)

Ground truth (correct) target values.

y_predarray-like, shape (n_samples,)

Predicted target values from the classifier.

labelslist 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.

digitsint, optional, default=3

Number of digits for formatting floating point values in the plots.

titlestring, optional, default=’train’

Title of the generated plot.

title_fontsizestring or int, optional, default=”large”

Font size for the plot title. Use e.g. “small”, “medium”, “large” or integer values.

text_fontsizestring or int, optional, default=”medium”

Font size for the text in the plot. Use e.g. “small”, “medium”, “large” or integer values.

cmapNone, str or matplotlib.colors.Colormap, optional, default=None

Colormap used for plotting. Options include ‘viridis’, ‘PiYG’, ‘plasma’, ‘inferno’, ‘nipy_spectral’, etc. See Matplotlib Colormap documentation for available choices.

x_tick_rotationint, optional, default=0

Rotates x-axis tick labels by the specified angle.

Added in version 0.3.9.

**kwargs: dict

Generic keyword arguments.

Returns:
figmatplotlib.figure.Figure

The figure on which the plot was drawn.

Other Parameters:
axmatplotlib.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).

Added in version 0.4.0.

figmatplotlib.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 0.4.0.

figsizetuple, optional, default=None

Width, height in inches. Tuple denoting figure size of the plot e.g. (12, 5)

Added in version 0.4.0.

nrowsint, optional, default=1

Number of rows in the subplot grid.

Added in version 0.4.0.

ncolsint, optional, default=1

Number of columns in the subplot grid.

Added in version 0.4.0.

plot_stylestr, optional, default=None

Check available styles with “plt.style.available”. Examples include: [‘ggplot’, ‘seaborn’, ‘bmh’, ‘classic’, ‘dark_background’, ‘fivethirtyeight’, ‘grayscale’, ‘seaborn-bright’, ‘seaborn-colorblind’, ‘seaborn-dark’, ‘seaborn-dark-palette’, ‘tableau-colorblind10’, ‘fast’].

Added in version 0.4.0.

show_figbool, default=True

Show the plot.

Added in version 0.4.0.

save_figbool, default=False

Save the plot.

Added in version 0.4.0.

save_fig_filenamestr, optional, default=’’

Specify the path and filetype to save the plot. If nothing specified, the plot will be saved as png inside result_images under to the current working directory. Defaults to plot image named to used func.__name__.

Added in version 0.4.0.

overwritebool, optional, default=True

If False and a file exists, auto-increments the filename to avoid overwriting.

Added in version 0.4.0.

add_timestampbool, optional, default=False

Whether to append a timestamp to the filename. Default is False.

Added in version 0.4.0.

verbosebool, optional

If True, enables verbose output with informative messages during execution. Useful for debugging or understanding internal operations such as backend selection, font loading, and file saving status. If False, runs silently unless errors occur.

Default is False.

Added in version 0.4.0: The verbose parameter was added to control logging and user feedback verbosity.

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)

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