DecisionBoundaryDisplay#

classsklearn.inspection.DecisionBoundaryDisplay(*, xx0, xx1, response, multiclass_colors=None, xlabel=None, ylabel=None)[source] #

Decisions boundary visualization.

It is recommended to use from_estimator to create a DecisionBoundaryDisplay. All parameters are stored as attributes.

Read more in the User Guide.

For a detailed example comparing the decision boundaries of multinomial and one-vs-rest logistic regression, please see Decision Boundaries of Multinomial and One-vs-Rest Logistic Regression.

Added in version 1.1.

Parameters:

xx0ndarray of shape (grid_resolution, grid_resolution)

First output of meshgrid.

xx1ndarray of shape (grid_resolution, grid_resolution)

Second output of meshgrid.

responsendarray of shape (grid_resolution, grid_resolution) or (grid_resolution, grid_resolution, n_classes)

Values of the response function.

multiclass_colorslist of str or str, default=None

Specifies how to color each class when plotting all classes of multiclass problem. Ignored for binary problems and multiclass problems when plotting a single prediction value per point. Possible inputs are:

• list: list of Matplotlib color strings, of length n_classes

• str: name of matplotlib.colors.Colormap

• None: ‘viridis’ colormap is used to sample colors

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.

Added in version 1.7.

xlabelstr, default=None

Default label to place on x axis.

ylabelstr, default=None

Default label to place on y axis.

Attributes:

surface_matplotlib QuadContourSet or QuadMesh or list of such objects

If plot_method is ‘contour’ or ‘contourf’, surface_ is QuadContourSet. If plot_method is ‘pcolormesh’, surface_ is QuadMesh.

multiclass_colors_array of shape (n_classes, 4)

Colors used to plot each class in multiclass problems. Only defined when color_of_interest is None.

Added in version 1.7.

ax_matplotlib Axes

Axes with decision boundary.

figure_matplotlib Figure

Figure containing the decision boundary.

See also

DecisionBoundaryDisplay.from_estimator

Plot decision boundary given an estimator.

Examples

>>> importmatplotlib.pyplotasplt
>>> importnumpyasnp
>>> fromsklearn.datasetsimport load_iris
>>> fromsklearn.inspectionimport DecisionBoundaryDisplay
>>> fromsklearn.treeimport 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, response=y_pred
... )
>>> display.plot()
<...>
>>> display.ax_.scatter(
...  iris.data[:, 0], iris.data[:, 1], c=iris.target, edgecolor="black"
... )
<...>
>>> plt.show()

../../_images/sklearn-inspection-DecisionBoundaryDisplay-1.png

classmethodfrom_estimator(estimator, X, *, grid_resolution=100, eps=1.0, plot_method='contourf', response_method='auto', class_of_interest=None, multiclass_colors=None, xlabel=None, ylabel=None, ax=None, **kwargs)[source] #

Plot decision boundary given an estimator.

Read more in the User Guide.

Parameters:

estimatorobject

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_resolutionint, 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.

epsfloat, 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: contourf, contour, pcolormesh.

response_method{‘auto’, ‘decision_function’, ‘predict_proba’, ‘predict’}, default=’auto’

Specifies whether to use decision_function, predict_proba or predict as the target response. If set to ‘auto’, the response method is tried in the order as listed above.

Changed in version 1.6: For multiclass problems, ‘auto’ no longer defaults to ‘predict’.

class_of_interestint, 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.

Added in version 1.4.

multiclass_colorslist of str, or str, default=None

Specifies how to color each class when plotting multiclass ‘predict_proba’ or ‘decision_function’ and class_of_interest is None. Ignored in all other cases.

Possible inputs are:

• list: list of Matplotlib color strings, of length n_classes

• str: name of 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.

Added in version 1.7.

xlabelstr, 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.

ylabelstr, 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.

axMatplotlib axes, default=None

Axes object to plot on. If None, a new figure and axes is created.

**kwargsdict

Additional keyword arguments to be passed to the plot_method.

Returns:

displayDecisionBoundaryDisplay

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

>>> importmatplotlib.pyplotasplt
>>> fromsklearn.datasetsimport load_iris
>>> fromsklearn.linear_modelimport LogisticRegression
>>> fromsklearn.inspectionimport 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,
... )
>>> disp.ax_.scatter(X[:, 0], X[:, 1], c=iris.target, edgecolor="k")
<...>
>>> plt.show()

../../_images/sklearn-inspection-DecisionBoundaryDisplay-2.png

plot(plot_method='contourf', ax=None, xlabel=None, ylabel=None, **kwargs)[source] #

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: contourf, contour, pcolormesh.

axMatplotlib axes, default=None

Axes object to plot on. If None, a new figure and axes is created.

xlabelstr, default=None

Overwrite the x-axis label.

ylabelstr, default=None

Overwrite the y-axis label.

**kwargsdict

Additional keyword arguments to be passed to the plot_method.

Returns:

display: DecisionBoundaryDisplay

Object that stores computed values.