Repository: scikit-learn/scikit-learn
File: sklearn/inspection/_plot/decision_boundary.py (Python)

Code before:
            :func:`contour <matplotlib.pyplot.contour>`,
            :func:`pcolormesh <matplotlib.pyplot.pcolormesh>`.

        response_method : {'auto', 'decision_function', 'predict_proba', \
                'predict'}, default='auto'
            Specifies whether to use :term:`decision_function`,
            :term:`predict_proba` or :term:`predict` as the target response.
            If set to 'auto', the response method is tried in the order as
            listed above.

            .. versionchanged:: 1.6
                For multiclass problems, 'auto' no longer defaults to 'predict'.

        class_of_interest : int, float, bool or str, default=None
            The class to be plotted when `response_method` is 'predict_proba'
            or 'decision_function'. If None, `estimator.classes_[1]` is considered
            the positive class for binary classifiers. For multiclass
            classifiers, if None, all classes will be represented in the
            decision boundary plot; the class with the highest response value
            at each point is plotted. The color of each class can be set via
            `multiclass_colors`.

            .. versionadded:: 1.4

        multiclass_colors : list of str, or str, default=None
            Specifies how to color each class when plotting multiclass problems
            and `class_of_interest` is None. Ignored in all other cases.

            Possible inputs are:

            * list: list of Matplotlib
              `color <https://matplotlib.org/stable/users/explain/colors/colors.html#colors-def>`_
              strings, of length `n_classes`
            * str: name of :class:`matplotlib.colors.Colormap`
            * None: 'tab10' colormap is used to sample colors if the number of
                classes is less than or equal to 10, otherwise 'gist_rainbow'
                colormap.

            Single color colormaps will be generated from the colors in the list or
            colors taken from the colormap, and passed to the `cmap` parameter of
            the `plot_method`.

            .. versionadded:: 1.7

        xlabel : str, default=None
            The label used for the x-axis. If `None`, an attempt is made to
            extract a label from `X` if it is a dataframe, otherwise an empty
            string is used.

        ylabel : str, default=None
            The label used for the y-axis. If `None`, an attempt is made to

Review comment (suggestion):
```suggestion
            Specifies how to color each class when plotting :term:`multiclass` problems
```

Code after:
            Higher values will make the plot look nicer but be slower to
            render.

        eps : float, default=1.0
            Extends the minimum and maximum values of X for evaluating the
            response function.

        plot_method : {'contourf', 'contour', 'pcolormesh'}, default='contourf'
            Plotting method to call when plotting the response. Please refer
            to the following matplotlib documentation for details:
            :func:`contourf <matplotlib.pyplot.contourf>`,
            :func:`contour <matplotlib.pyplot.contour>`,
            :func:`pcolormesh <matplotlib.pyplot.pcolormesh>`.

        response_method : {'auto', 'decision_function', 'predict_proba', \
                'predict'}, default='auto'
            Specifies whether to use :term:`decision_function`,
            :term:`predict_proba` or :term:`predict` as the target response.
            If set to 'auto', the response method is tried in the order as
            listed above.

            .. versionchanged:: 1.6
                For multiclass problems, 'auto' no longer defaults to 'predict'.

        class_of_interest : int, float, bool or str, default=None
            The class to be plotted. For :term:`binary` classifiers, if None,
            `estimator.classes_[1]` is considered the positive class. For
            :term:`multiclass` classifiers, if None, all classes will be represented in
            the decision boundary plot; when `response_method` is :term:`predict_proba`
            or :term:`decision_function`, the class with the highest response value
            at each point is plotted. The color of each class can be set via
            `multiclass_colors`.

            .. versionadded:: 1.4

        multiclass_colors : list of str, or str, default=None
            Specifies how to color each class when plotting :term:`multiclass` problems
            and `class_of_interest` is None.

            Possible inputs are:

            * list: list of Matplotlib
              `color <https://matplotlib.org/stable/users/explain/colors/colors.html#colors-def>`_
              strings, of length `n_classes`
            * str: name of :class:`matplotlib.colors.Colormap`
            * None: 'tab10' colormap is used to sample colors if the number of
                classes is less than or equal to 10, otherwise 'gist_rainbow'
                colormap.

            Single color (fading to white) colormaps will be generated from the colors
            in the list or colors taken from the colormap, and passed to the `cmap`