from sklearn.kernel_ridge import KernelRidge from sklearn.metrics import mean_squared_error X = [[0, 0], [2, 2], [3, 3]] y = [0, 2, 3] clf = KernelRidge(alpha=1.0) clf.fit(X, y) y_pred = clf.predict([[4, 4]]) score = clf.score(X, y) mse = mean_squared_error(y, clf.predict(X)) print("Score:", score) print("MSE:", mse)
from sklearn.kernel_ridge import KernelRidge from sklearn.datasets import make_regression from sklearn.model_selection import cross_val_score X, y = make_regression(n_samples=100, n_features=10, n_informative=5, noise=0.5) clf = KernelRidge(alpha=1.0) scores = cross_val_score(clf, X, y, cv=5) print("Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2))In this example, we create a synthetic dataset using make_regression() function. Then, we create an object of the kernel ridge regression model with alpha=1.0 hyperparameter. We use cross_val_score() function to evaluate the model using 5-fold cross-validation. Finally, we print the mean accuracy score and standard deviation of the 5-fold cross-validation. In conclusion, the sklearn.kernel_ridge package is a powerful library for solving regression problems using kernel ridge regression in Python. It provides a range of hyperparameters to control the model's behavior and several evaluation functions to assess the model's performance.