sklearn/inspection/_plot/decision_boundary.py - main - githubmirror/scikit-learn

sklearn/inspection/_plot/decision_boundary.py
854 строки34 KB
Marco Edward Gorelli
MNT Use pyrefly instead of mypy for type-checking (#34527)
10 авг 2026, 16:51
10 авг 2026, 16:5194a85a8
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause

import warnings

import narwhals.stable.v2 as nw
import numpy as np

from sklearn.base import is_classifier, is_clusterer, is_outlier_detector, is_regressor
from sklearn.preprocessing import LabelEncoder
from sklearn.utils import _safe_indexing
from sklearn.utils._dataframe import is_polars_df
from sklearn.utils._optional_dependencies import check_matplotlib_support
from sklearn.utils._response import _get_response_values
from sklearn.utils._set_output import _get_adapter_from_container
from sklearn.utils.deprecation import deprecated
from sklearn.utils.fixes import PETROFF_COLORS
from sklearn.utils.multiclass import type_of_target
from sklearn.utils.validation import (
    _is_arraylike_not_scalar,
    _num_features,
    check_is_fitted,
)


def _check_boundary_response_method(estimator, response_method):
    """Validate the response methods to be used with the fitted estimator.

    Parameters
    ----------
    estimator : object
        Fitted estimator to check.

    response_method : {'auto', 'decision_function', 'predict_proba', 'predict'}
        Specifies whether to use :term:`decision_function`, :term:`predict_proba`,
        :term:`predict` as the target response. If set to 'auto', the response method is
        tried in the before mentioned order.

    Returns
    -------
    prediction_method : list of str or str
        The name or list of names of the response methods to use.
    """
    has_classes = hasattr(estimator, "classes_")
    if has_classes and _is_arraylike_not_scalar(estimator.classes_[0]):
        msg = "Multi-label and multi-output multi-class classifiers are not supported"
        raise ValueError(msg)

    if response_method == "auto":
        if is_regressor(estimator):
            prediction_method = "predict"
        else:
            prediction_method = ["decision_function", "predict_proba", "predict"]
    else:
        prediction_method = response_method

    return prediction_method


# TODO(1.12): remove
def _deprecate_multiclass_colors(multiclass_colors, target_colors):
    """Handle deprecation of `multiclass_colors` renamed to `target_colors`."""
    if multiclass_colors != "deprecated":
        warnings.warn(
            "'multiclass_colors' was renamed to 'target_colors' in 1.10 and will be "
            "removed in 1.12.",
            FutureWarning,
        )
        if target_colors is not None:
            raise ValueError(
                "'multiclass_colors' and 'target_colors' cannot be used together. "
                "Pass `target_colors` only."
            )
        return multiclass_colors
    return target_colors


def _select_colors(mpl, target_colors, n_classes):
    """Select colors for multiclass decision boundary display.

    Parameters
    ----------
    mpl : module
        Imported `matplotlib` module.

    target_colors : str or list of matplotlib colors, default=None
        The colormap or colors to select.

        Possible inputs are:

        * None: defaults to list of accessible `Petroff colors
          <https://github.com/matplotlib/matplotlib/issues/9460#issuecomment-875185352>`_
          if `n_classes <= 10`, otherwise 'gist_rainbow' colormap
        * str: name of :class:`matplotlib.colors.Colormap`
        * list: list of length `n_classes` of `matplotlib colors
          <https://matplotlib.org/stable/users/explain/colors/colors.html#colors-def>`_

    n_classes : int
        Number of colors to select.

    Returns
    -------
    colors : ndarray of shape (n_classes, 4)
        RGBA colors, one per class.

    """

    if target_colors is None:
        # select accessible colors according to Matthew A. Petroff, see
        # https://arxiv.org/abs/2107.02270 and
        # https://github.com/matplotlib/matplotlib/issues/9460#issuecomment-875185352
        if n_classes <= 10:
            target_colors = PETROFF_COLORS[:n_classes]
        else:
            target_colors = "gist_rainbow"

    if isinstance(target_colors, str):
        if target_colors not in mpl.pyplot.colormaps():
            raise ValueError(
                "When 'target_colors' is a string, it must be a valid "
                f"Matplotlib colormap. Got: {target_colors}"
            )
        cmap = mpl.pyplot.get_cmap(target_colors)
        if cmap.N < n_classes:
            raise ValueError(
                f"Colormap '{target_colors}' only has {cmap.N} colors, but "
                f"{n_classes} classes are to be displayed. Please specify a "
                "different colormap or provide a list of colors via "
                "'target_colors'."
            )
        return cmap(np.linspace(0, 1, n_classes))

    elif isinstance(target_colors, list):
        if len(target_colors) != n_classes:
            raise ValueError(
                "When 'target_colors' is a list, it must be of the same "
                f"length as the classes or labels to plot ({n_classes}), got: "
                f"{len(target_colors)}."
            )
        elif any(not mpl.colors.is_color_like(col) for col in target_colors):
            raise ValueError(
                "When 'target_colors' is a list, it can only contain valid"
                f" Matplotlib color names. Got: {target_colors}"
            )
        return mpl.colors.to_rgba_array(target_colors)

    else:
        raise TypeError("'target_colors' must be a list or a str.")


class DecisionBoundaryDisplay:
    """Decisions boundary visualization.

    It is recommended to use
    :func:`~sklearn.inspection.DecisionBoundaryDisplay.from_estimator`
    to create a :class:`DecisionBoundaryDisplay`. All parameters are stored as
    attributes.

    Read more in the :ref:`User Guide <visualizations>`.

    For a detailed example comparing the decision boundaries of multinomial and
    one-vs-rest logistic regression, please see
    :ref:`sphx_glr_auto_examples_linear_model_plot_logistic_multinomial.py`.

    .. versionadded:: 1.1

    Parameters
    ----------
    xx0 : ndarray of shape (grid_resolution, grid_resolution)
        First output of :func:`meshgrid <numpy.meshgrid>`.

    xx1 : ndarray of shape (grid_resolution, grid_resolution)
        Second output of :func:`meshgrid <numpy.meshgrid>`.

    n_classes : int
        Expected number of unique classes or labels if `response` was generated by a
        :term:`classifier` or a :term:`clusterer`.

        For :term:`outlier detectors`, `n_classes` should be set to 2 by definition
        (inlier or outlier).

        For :term:`regressors`, `n_classes` should also be set to 2 by convention
        (continuous responses are displayed the same way as unthresholded binary
        responses).

        .. versionadded:: 1.9

    response : ndarray of shape (grid_resolution, grid_resolution) or \
            (grid_resolution, grid_resolution, n_classes)
        Values of the response function.

    target_colors : str or list of matplotlib colors, default=None
        Specifies how to color each class when plotting all classes of
        :term:`multiclass` problems.

        Possible inputs are:

        * None: defaults to list of accessible `Petroff colors
          <https://github.com/matplotlib/matplotlib/issues/9460#issuecomment-875185352>`_
          if `n_classes <= 10`, otherwise 'gist_rainbow' colormap
        * str: name of :class:`matplotlib.colors.Colormap`
        * list: list of length `n_classes` of `matplotlib colors
          <https://matplotlib.org/stable/users/explain/colors/colors.html#colors-def>`_

        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` parameter
        of the `plot_method`.

        When `response_method='predict'` and `plot_method='contour'`,
        `target_colors` is ignored and the class boundaries are plotted in black
        instead as the boundary lines may overlap and the colors don't necessarily
        correspond to the classes.

        For :term:`binary` problems, `target_colors` is also ignored and `cmap` or
        `colors` can be passed as kwargs instead, otherwise, the default colormap
        ('viridis') is used.

        .. versionadded:: 1.10
            `multiclass_colors` was renamed to `target_colors`

    xlabel : str, default=None
        Default label to place on x axis.

    ylabel : str, default=None
        Default label to place on y axis.

    multiclass_colors : str or list of matplotlib colors, default=None
        Specifies how to color each class when plotting all classes of
        :term:`multiclass` problems.

        Possible inputs are:

        * None: defaults to list of accessible `Petroff colors
          <https://github.com/matplotlib/matplotlib/issues/9460#issuecomment-875185352>`_
          if `n_classes <= 10`, otherwise 'gist_rainbow' colormap
        * str: name of :class:`matplotlib.colors.Colormap`
        * list: list of length `n_classes` of `matplotlib colors
          <https://matplotlib.org/stable/users/explain/colors/colors.html#colors-def>`_

        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` parameter
        of the `plot_method`.

        When `response_method='predict'` and `plot_method='contour'`,
        `target_colors` is ignored and the class boundaries are plotted in black
        instead as the boundary lines may overlap and the colors don't necessarily
        correspond to the classes.

        For :term:`binary` problems, `target_colors` is also ignored and `cmap` or
        `colors` can be passed as kwargs instead, otherwise, the default colormap
        ('viridis') is used.

        .. versionadded:: 1.7
        .. versionchanged:: 1.9
            `target_colors` is now also used when `response_method="predict"`,
            except for when `plot_method='contour'`, where it is ignored and "black" is
            used instead.
            The default colors changed from 'tab10' to the more accessible `Petroff
            colors <https://github.com/matplotlib/matplotlib/issues/9460#issuecomment-875185352>`_.
        .. deprecated:: 1.10
            `multiclass_colors` was renamed to `target_colors` in 1.10 and will be
            removed in 1.12.

    Attributes
    ----------
    surface_ : matplotlib `QuadContourSet` or `QuadMesh` or list of such objects
        If `plot_method` is 'contour' or 'contourf', `surface_` is
        :class:`QuadContourSet <matplotlib.contour.QuadContourSet>`. If
        `plot_method` is 'pcolormesh', `surface_` is
        :class:`QuadMesh <matplotlib.collections.QuadMesh>`.

    target_colors_ : array of shape (n_classes, 4)
        Colors used to plot each class in multiclass problems.
        Only defined when `n_classes` > 2.

        .. versionadded:: 1.10
            `multiclass_colors_` was renamed to `target_colors_`

    ax_ : matplotlib Axes
        Axes with decision boundary.

    figure_ : matplotlib Figure
        Figure containing the decision boundary.

    multiclass_colors_ : array of shape (n_classes, 4)
        Colors used to plot each class in multiclass problems.
        Only defined when `n_classes` > 2.

        .. versionadded:: 1.7
        .. deprecated:: 1.10
            `multiclass_colors_` was renamed to `target_colors_` in 1.10 and will be
            removed in 1.12.

    See Also
    --------
    DecisionBoundaryDisplay.from_estimator : Plot decision boundary given an estimator.

    Examples
    --------
    >>> import matplotlib.pyplot as plt
    >>> import matplotlib as mpl
    >>> import numpy as np
    >>> from sklearn.linear_model import LogisticRegression
    >>> from sklearn.inspection import DecisionBoundaryDisplay
    >>> data = np.array([[0, 0], [1, 1], [2, 1], [2, 2], [3, 2], [3, 3]])
    >>> target = np.arange(data.shape[0])
    >>> clf = LogisticRegression().fit(data, target)
    >>> plot_methods = ["contourf", "contour", "pcolormesh"]
    >>> response_methods = ["predict_proba", "decision_function", "predict"]
    >>> _, axes = plt.subplots(
    ...     nrows=3,
    ...     ncols=3,
    ...     figsize=(12, 12),
    ...     constrained_layout=True
    ... )
    >>> for plot_method_idx, plot_method in enumerate(plot_methods):
    ...     for response_method_idx, response_method in enumerate(response_methods):
    ...         ax = axes[plot_method_idx, response_method_idx]
    ...         display = DecisionBoundaryDisplay.from_estimator(
    ...             clf,
    ...             data,
    ...             grid_resolution=300,
    ...             response_method=response_method,
    ...             plot_method=plot_method,
    ...             ax=ax,
    ...             alpha=0.5,
    ...         )
    ...         cmap = mpl.colors.ListedColormap(display.target_colors_)
    ...         ax.scatter(
    ...             data[:, 0],
    ...             data[:, 1],
    ...             c=target.astype(int),
    ...             edgecolors="black",
    ...             cmap=cmap,
    ...         )
    ...         ax.set_title(
    ...             f"plot_method={plot_method}\\nresponse_method={response_method}"
    ...         )
    >>> plt.show()
    """

    def __init__(
        self,
        *,
        xx0,
        xx1,
        n_classes,
        response,
        target_colors=None,
        xlabel=None,
        ylabel=None,
        multiclass_colors="deprecated",  # TODO(1.12): remove
    ):
        self.xx0 = xx0
        self.xx1 = xx1
        self.n_classes = n_classes
        self.response = response
        # TODO(1.12): remove and replace with `self.target_colors = target_colors`
        self.target_colors = _deprecate_multiclass_colors(
            multiclass_colors, target_colors
        )
        self.xlabel = xlabel
        self.ylabel = ylabel

    # TODO(1.12): remove
    @deprecated(
        "Attribute `multiclass_colors_` was renamed to `target_colors_` in 1.10 and"
        " will be removed in 1.12. Use `target_colors_` instead."
    )
    @property
    def multiclass_colors_(self):
        return self.target_colors_

    def plot(self, plot_method="contourf", ax=None, xlabel=None, ylabel=None, **kwargs):
        """Plot visualization.

        Parameters
        ----------
        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>`.

        ax : Matplotlib axes, default=None
            Axes object to plot on. If `None`, a new figure and axes is
            created.

        xlabel : str, default=None
            Overwrite the x-axis label.

        ylabel : str, default=None
            Overwrite the y-axis label.

        **kwargs : dict
            Additional keyword arguments to be passed to the `plot_method`. For
            :term:`binary` problems, `cmap` or `colors` can be set here to specify the
            colormap or colors, otherwise the default colormap ('viridis') is used. If
            not specified by the user, `zorder` is set to -1 to ensure that the decision
            boundary is plotted in the background (in case a scatter plot is added on
            top).

        Returns
        -------
        display: :class:`~sklearn.inspection.DecisionBoundaryDisplay`
            Object that stores computed values.

        See Also
        --------
        DecisionBoundaryDisplay.from_estimator : Plot decision boundary given an
            estimator.

        Examples
        --------
        >>> import matplotlib as mpl
        >>> import matplotlib.pyplot as plt
        >>> import numpy as np
        >>> from sklearn.datasets import load_iris
        >>> from sklearn.inspection import DecisionBoundaryDisplay
        >>> from sklearn.tree import DecisionTreeClassifier
        >>> iris = load_iris()
        >>> feature_1, feature_2 = np.meshgrid(
        ...     np.linspace(iris.data[:, 0].min(), iris.data[:, 0].max()),
        ...     np.linspace(iris.data[:, 1].min(), iris.data[:, 1].max())
        ... )
        >>> grid = np.vstack([feature_1.ravel(), feature_2.ravel()]).T
        >>> tree = DecisionTreeClassifier().fit(iris.data[:, :2], iris.target)
        >>> y_pred = np.reshape(tree.predict(grid), feature_1.shape)
        >>> display = DecisionBoundaryDisplay(
        ...     xx0=feature_1,
        ...     xx1=feature_2,
        ...     n_classes=len(tree.classes_),
        ...     response=y_pred
        ... )
        >>> display.plot()
        <...>
        >>> display.ax_.scatter(
        ...     iris.data[:, 0],
        ...     iris.data[:, 1],
        ...     c=iris.target,
        ...     cmap=mpl.colors.ListedColormap(display.target_colors_),
        ...     edgecolor="black"
        ... )
        <...>
        >>> plt.show()
        """
        check_matplotlib_support("DecisionBoundaryDisplay.plot")
        import matplotlib as mpl
        import matplotlib.pyplot as plt

        if plot_method not in ("contourf", "contour", "pcolormesh"):
            raise ValueError(
                "plot_method must be 'contourf', 'contour', or 'pcolormesh'. "
                f"Got {plot_method} instead."
            )

        if ax is None:
            _, ax = plt.subplots()

        plot_func = getattr(ax, plot_method)
        if self.n_classes == 2:
            self.surface_ = plot_func(self.xx0, self.xx1, self.response, **kwargs)
        else:  # multiclass
            for kwarg in ("cmap", "colors"):
                if kwarg in kwargs:
                    warnings.warn(
                        f"'{kwarg}' is ignored in favor of 'target_colors' "
                        "in the multiclass case."
                    )
                    del kwargs[kwarg]

            self.target_colors_ = _select_colors(
                mpl, self.target_colors, self.n_classes
            )

            # If not set by the user, set default values for `zorder` to ensure that the
            # decision boundary is plotted in the background (in case a scatter plot is
            # added on top)
            if "zorder" not in kwargs:
                kwargs["zorder"] = -1

            if self.response.ndim == 3:  # predict_proba and decision_function
                multiclass_cmaps = [
                    mpl.colors.LinearSegmentedColormap.from_list(
                        f"colormap_{class_idx}",
                        [(1.0, 1.0, 1.0, 1.0), (r, g, b, 1.0)],
                    )
                    for class_idx, (r, g, b, _) in enumerate(self.target_colors_)
                ]
                self.surface_ = []
                for class_idx, cmap in enumerate(multiclass_cmaps):
                    response = np.ma.array(
                        self.response[:, :, class_idx],
                        mask=(self.response.argmax(axis=2) != class_idx),
                    )
                    self.surface_.append(
                        plot_func(self.xx0, self.xx1, response, cmap=cmap, **kwargs)
                    )

                if plot_method == "contour":
                    # Additionally plot the decision boundaries between classes.
                    self.surface_.append(
                        plot_func(
                            self.xx0,
                            self.xx1,
                            self.response.argmax(axis=2),
                            colors="black",
                            zorder=-1,
                            # set levels to ensure all boundaries are plotted correctly
                            levels=np.arange(self.n_classes),
                        )
                    )

            elif self.response.ndim == 2:  # predict
                # Set `levels` to ensure all class boundaries are displayed.
                if "levels" not in kwargs:
                    if plot_method == "contour":
                        kwargs["levels"] = np.arange(self.n_classes)
                    elif plot_method == "contourf":
                        kwargs["levels"] = np.arange(self.n_classes + 1) - 0.5

                if plot_method == "contour":
                    self.surface_ = plot_func(
                        self.xx0, self.xx1, self.response, colors="black", **kwargs
                    )
                else:
                    # `pcolormesh` requires cmap, for `contourf` it makes no difference
                    cmap = mpl.colors.ListedColormap(self.target_colors_)
                    self.surface_ = plot_func(
                        self.xx0, self.xx1, self.response, cmap=cmap, **kwargs
                    )

        if xlabel is not None or not ax.get_xlabel():
            xlabel = self.xlabel if xlabel is None else xlabel
            ax.set_xlabel(xlabel)
        if ylabel is not None or not ax.get_ylabel():
            ylabel = self.ylabel if ylabel is None else ylabel
            ax.set_ylabel(ylabel)

        self.ax_ = ax
        self.figure_ = ax.figure
        return self

    @classmethod
    def from_estimator(
        cls,
        estimator,
        X,
        *,
        grid_resolution=100,
        eps=1.0,
        plot_method="contourf",
        response_method="auto",
        class_of_interest=None,
        target_colors=None,
        xlabel=None,
        ylabel=None,
        ax=None,
        multiclass_colors="deprecated",  # TODO(1.12): remove
        **kwargs,
    ):
        """Plot decision boundary given an estimator.

        Read more in the :ref:`User Guide <visualizations>`.

        Parameters
        ----------
        estimator : object
            Trained estimator used to plot the decision boundary.

        X : {array-like, sparse matrix, dataframe} of shape (n_samples, 2)
            Input data that should be only 2-dimensional.

        grid_resolution : int, default=100
            Number of grid points to use for plotting decision boundary.
            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
            `target_colors`.

            .. versionadded:: 1.4

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

            Possible inputs are:

            * None: defaults to list of accessible `Petroff colors
              <https://github.com/matplotlib/matplotlib/issues/9460#issuecomment-875185352>`_
              if `n_classes <= 10`, otherwise 'gist_rainbow' colormap
            * str: name of :class:`matplotlib.colors.Colormap`
            * list: list of length `n_classes` of `matplotlib colors
              <https://matplotlib.org/stable/users/explain/colors/colors.html#colors-def>`_

            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`
            parameter of the `plot_method`.

            When `response_method='predict'` and `plot_method='contour'`,
            `target_colors` is ignored and the class boundaries are plotted in black
            instead as the boundary lines may overlap and the colors don't necessarily
            correspond to the classes.

            For :term:`binary` problems, `target_colors` is also ignored and `cmap`
            or `colors` can be passed as kwargs instead, otherwise, the default colormap
            ('viridis') is used.

            .. versionadded:: 1.10
                `multiclass_colors` was renamed to `target_colors`

        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
            extract a label from `X` if it is a dataframe, otherwise an empty
            string is used.

        ax : Matplotlib axes, default=None
            Axes object to plot on. If `None`, a new figure and axes is
            created.

        multiclass_colors : str or list of matplotlib colors, default=None
            Specifies how to color each class when plotting all classes of
            :term:`multiclass` problems.

            Possible inputs are:

            * None: defaults to list of accessible `Petroff colors
              <https://github.com/matplotlib/matplotlib/issues/9460#issuecomment-875185352>`_
              if `n_classes <= 10`, otherwise 'gist_rainbow' colormap
            * str: name of :class:`matplotlib.colors.Colormap`
            * list: list of length `n_classes` of `matplotlib colors
              <https://matplotlib.org/stable/users/explain/colors/colors.html#colors-def>`_

            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`
            parameter of the `plot_method`.

            When `response_method='predict'` and `plot_method='contour'`,
            `target_colors` is ignored and the class boundaries are plotted in black
            instead as the boundary lines may overlap and the colors don't necessarily
            correspond to the classes.

            For :term:`binary` problems, `target_colors` is also ignored and `cmap` or
            `colors` can be passed as kwargs instead, otherwise, the default colormap
            ('viridis') is used.

            .. versionadded:: 1.7
            .. versionchanged:: 1.9
                `target_colors` is now also used when `response_method="predict"`,
                except for when `plot_method='contour'`, where it is ignored and "black"
                is used instead.
                The default colors changed from 'tab10' to the more accessible `Petroff
                colors <https://github.com/matplotlib/matplotlib/issues/9460#issuecomment-875185352>`_.
            .. deprecated:: 1.10
                `multiclass_colors` was renamed to `target_colors` in 1.10 and will be
                removed in 1.12.

        **kwargs : dict
            Additional keyword arguments to be passed to the `plot_method`.

        Returns
        -------
        display : :class:`~sklearn.inspection.DecisionBoundaryDisplay`
            Object that stores the result.

        See Also
        --------
        DecisionBoundaryDisplay : Decision boundary visualization.
        sklearn.metrics.ConfusionMatrixDisplay.from_estimator : Plot the
            confusion matrix given an estimator, the data, and the label.
        sklearn.metrics.ConfusionMatrixDisplay.from_predictions : Plot the
            confusion matrix given the true and predicted labels.

        Examples
        --------
        >>> import matplotlib as mpl
        >>> import matplotlib.pyplot as plt
        >>> from sklearn.datasets import load_iris
        >>> from sklearn.linear_model import LogisticRegression
        >>> from sklearn.inspection import DecisionBoundaryDisplay
        >>> iris = load_iris()
        >>> X = iris.data[:, :2]
        >>> classifier = LogisticRegression().fit(X, iris.target)
        >>> disp = DecisionBoundaryDisplay.from_estimator(
        ...     classifier, X, response_method="predict",
        ...     xlabel=iris.feature_names[0], ylabel=iris.feature_names[1],
        ...     alpha=0.5,
        ... )
        >>> cmap = mpl.colors.ListedColormap(disp.target_colors_)
        >>> disp.ax_.scatter(X[:, 0], X[:, 1], c=iris.target, edgecolor="k", cmap=cmap)
        <...>
        >>> plt.show()
        """
        check_is_fitted(estimator)

        if not grid_resolution > 1:
            raise ValueError(
                "grid_resolution must be greater than 1. Got"
                f" {grid_resolution} instead."
            )

        if not eps >= 0:
            raise ValueError(
                f"eps must be greater than or equal to 0. Got {eps} instead."
            )

        possible_plot_methods = ("contourf", "contour", "pcolormesh")
        if plot_method not in possible_plot_methods:
            available_methods = ", ".join(possible_plot_methods)
            raise ValueError(
                f"plot_method must be one of {available_methods}. "
                f"Got {plot_method} instead."
            )

        num_features = _num_features(X)
        if num_features != 2:
            raise ValueError(
                f"n_features must be equal to 2. Got {num_features} instead."
            )

        x0, x1 = _safe_indexing(X, 0, axis=1), _safe_indexing(X, 1, axis=1)

        x0_min, x0_max = x0.min() - eps, x0.max() + eps
        x1_min, x1_max = x1.min() - eps, x1.max() + eps

        xx0, xx1 = np.meshgrid(
            np.linspace(x0_min, x0_max, grid_resolution),
            np.linspace(x1_min, x1_max, grid_resolution),
        )

        X_grid = np.c_[xx0.ravel(), xx1.ravel()]
        if nw.dependencies.is_pandas_dataframe(X) or is_polars_df(X):
            adapter = _get_adapter_from_container(X)
            X_grid = adapter.create_container(
                X_grid,
                X_grid,
                columns=X.columns,
            )

        prediction_method = _check_boundary_response_method(estimator, response_method)
        if (class_of_interest is not None and hasattr(estimator, "classes_")) and (
            class_of_interest not in estimator.classes_
        ):
            raise ValueError(
                f"class_of_interest={class_of_interest} is not a valid label: It "
                f"should be one of {estimator.classes_}"
            )

        response, _, response_method_used = _get_response_values(
            estimator,
            X_grid,
            response_method=prediction_method,
            pos_label=class_of_interest,
            return_response_method_used=True,
        )

        # convert classes predictions into integers
        if response_method_used == "predict" and hasattr(estimator, "classes_"):
            encoder = LabelEncoder()
            encoder.classes_ = estimator.classes_
            response = encoder.transform(response)

        # infer n_classes from the estimator
        if (
            class_of_interest is not None
            or is_regressor(estimator)
            or is_outlier_detector(estimator)
        ):
            n_classes = 2
        elif is_classifier(estimator) and hasattr(estimator, "classes_"):
            n_classes = len(estimator.classes_)
        elif is_clusterer(estimator) and hasattr(estimator, "labels_"):
            n_classes = len(np.unique(estimator.labels_))
        else:
            target_type = type_of_target(response)
            if target_type in ("binary", "continuous"):
                n_classes = 2
            elif target_type == "multiclass":
                n_classes = len(np.unique(response))
            else:
                raise ValueError(
                    "Number of classes or labels cannot be inferred from "
                    f"{estimator.__class__.__name__}. Please make sure your estimator "
                    "follows scikit-learn's estimator API as described here: "
                    "https://scikit-learn.org/stable/developers/develop.html#rolling-your-own-estimator"
                )

        if response.ndim == 1:
            response = response.reshape(*xx0.shape)
        else:
            if is_regressor(estimator):
                raise ValueError("Multi-output regressors are not supported")

            if class_of_interest is not None:
                # For the multiclass case, `_get_response_values` returns the response
                # as-is. Thus, we have a column per class and we need to select the
                # column corresponding to the positive class.
                col_idx = np.flatnonzero(estimator.classes_ == class_of_interest)[0]
                response = response[:, col_idx].reshape(*xx0.shape)
            else:
                response = response.reshape(*xx0.shape, response.shape[-1])

        if xlabel is None:
            xlabel = X.columns[0] if hasattr(X, "columns") else ""

        if ylabel is None:
            ylabel = X.columns[1] if hasattr(X, "columns") else ""

        # TODO(1.12): remove and replace with direct use of `target_colors`
        target_colors = _deprecate_multiclass_colors(multiclass_colors, target_colors)
        display = cls(
            xx0=xx0,
            xx1=xx1,
            n_classes=n_classes,
            response=response,
            target_colors=target_colors,
            xlabel=xlabel,
            ylabel=ylabel,
        )
        return display.plot(ax=ax, plot_method=plot_method, **kwargs)