Note

Go to the end to download the full example code or to run this example in your browser via JupyterLite or Binder.

Gaussian process classification (GPC) on iris dataset#

This example illustrates the predicted probability of GPC for an isotropic and anisotropic RBF kernel on a two-dimensional version for the iris-dataset. The anisotropic RBF kernel obtains slightly higher log-marginal-likelihood by assigning different length-scales to the two feature dimensions.

Isotropic RBF, LML: -48.316, Anisotropic RBF, LML: -47.888

# 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)

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)

titles = ["Isotropic RBF", "Anisotropic RBF"]
plt.figure(figsize=(10, 5))

for i, clf in enumerate((gpc_rbf_isotropic, gpc_rbf_anisotropic)):
    plt.subplot(1, 2, i + 1)

    # 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
    )

    # 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",
    )

    plt.xlabel("Sepal length")
    plt.ylabel("Sepal width")
    plt.xticks(())
    plt.yticks(())
    plt.title(
        "%s, LML: %.3f" % (titles[i], clf.log_marginal_likelihood(clf.kernel_.theta))
    )

plt.tight_layout()
plt.show()