DOC Use DecisionBoundaryDisplay in plot_gpc_iris (scikit-learn#34509)

Co-authored-by: Stefanie Senger <91849487+StefanieSenger@users.noreply.github.com>

Author: mcvincekova

examples/gaussian_process/plot_gpc_iris.py

@@ -13,49 +13,48 @@
 # Authors: The scikit-learn developers
 # SPDX-License-Identifier: BSD-3-Clause
 
+import matplotlib as mpl
 import matplotlib.pyplot as plt
 import numpy as np
 
 from sklearn import datasets
 from sklearn.gaussian_process import GaussianProcessClassifier
 from sklearn.gaussian_process.kernels import RBF
+from sklearn.inspection import DecisionBoundaryDisplay
 
 # import some data to play with
 iris = datasets.load_iris()
 X = iris.data[:, :2]  # we only take the first two features.
-y = np.array(iris.target, dtype=int)
-
-h = 0.02  # step size in the mesh
+y = np.array(iris.target)
 
 kernel = 1.0 * RBF([1.0])
 gpc_rbf_isotropic = GaussianProcessClassifier(kernel=kernel).fit(X, y)
 kernel = 1.0 * RBF([1.0, 1.0])
 gpc_rbf_anisotropic = GaussianProcessClassifier(kernel=kernel).fit(X, y)
 
-# create a mesh to plot in
-x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
-y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
-xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
-
 titles = ["Isotropic RBF", "Anisotropic RBF"]
 plt.figure(figsize=(10, 5))
+
 for i, clf in enumerate((gpc_rbf_isotropic, gpc_rbf_anisotropic)):
-    # Plot the predicted probabilities. For that, we will assign a color to
-    # each point in the mesh [x_min, m_max]x[y_min, y_max].
     plt.subplot(1, 2, i + 1)
 
-    Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])
+    # Visualize the decision boundary as class regions shaded in the predicted
+    # probability of the winning class
+    boundary_display = DecisionBoundaryDisplay.from_estimator(
+        clf, X, ax=plt.gca(), response_method="predict_proba", alpha=0.5
+    )
 
-    # Put the result into a color plot
-    Z = Z.reshape((xx.shape[0], xx.shape[1], 3))
-    plt.imshow(Z, extent=(x_min, x_max, y_min, y_max), origin="lower")
+    # Plot also the training points reusing the boundary display color scheme
+    plt.scatter(
+        X[:, 0],
+        X[:, 1],
+        c=y,
+        cmap=mpl.colors.ListedColormap(boundary_display.target_colors_),
+        edgecolors="k",
+    )
 
-    # Plot also the training points
-    plt.scatter(X[:, 0], X[:, 1], c=np.array(["r", "g", "b"])[y], edgecolors=(0, 0, 0))
     plt.xlabel("Sepal length")
     plt.ylabel("Sepal width")
-    plt.xlim(xx.min(), xx.max())
-    plt.ylim(yy.min(), yy.max())
     plt.xticks(())
     plt.yticks(())
     plt.title(