DecisionBoundaryDisplay

classsklearn.inspection.DecisionBoundaryDisplay(*, xx0, xx1, n_classes, 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.

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.multiclass_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()

../../_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.

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.multiclass_colors_)
>>> disp.ax_.scatter(X[:, 0], X[:, 1], c=iris.target, edgecolor="k", cmap=cmap)
<...>
>>> 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. For 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: DecisionBoundaryDisplay: Object that stores computed values.

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.multiclass_colors_),
...     edgecolor="black"
... )
<...>
>>> plt.show()

../../_images/sklearn-inspection-DecisionBoundaryDisplay-3.png

Gallery examples

Outlier detection on a real data set

Plot classification probability

Classifier comparison

Linear and Quadratic Discriminant Analysis with covariance ellipsoid

Inductive Clustering

Two-class AdaBoost

IsolationForest example

Visualizing the probabilistic predictions of a VotingClassifier

Decision Boundaries of Multinomial and One-vs-Rest Logistic Regression

Plot multi-class SGD on the iris dataset

One-Class SVM versus One-Class SVM using Stochastic Gradient Descent

Class Likelihood Ratios to measure classification performance

Nearest Neighbors Classification

Comparing Nearest Neighbors with and without Neighborhood Components Analysis

Nearest Centroid Classification

Importance of Feature Scaling

Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset

SVM with custom kernel

Plot different SVM classifiers in the iris dataset

Plot the support vectors in LinearSVC

One-class SVM with non-linear kernel (RBF)

RBF SVM parameters

SVM: Maximum margin separating hyperplane

SVM: Separating hyperplane for unbalanced classes

Plot classification boundaries with different SVM Kernels

SVM: Weighted samples

Plot the decision surface of decision trees trained on the iris dataset

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Licensed under the 3-clause BSD License.
https://scikit-learn.org/1.9/modules/generated/sklearn.inspection.DecisionBoundaryDisplay.html