from sklearn import kernel_ridge import numpy as np # Create training data X_train = np.array([[0], [1], [2], [3]]) y_train = np.array([4, 3, 2, 1]) # Create testing data X_test = np.array([[4]]) # Train the model kr_model = kernel_ridge.KernelRidge(kernel='rbf') kr_model.fit(X_train, y_train) # Predict the value of the test data point kr_pred = kr_model.predict(X_test) print(kr_pred)In this example, we first create training and testing data. We then initialize a `KernelRidge` object with a radial basis function (RBF) kernel and fit the training data. Finally, we use the `predict()` function to make a prediction on a new data point and print the result. The package library used in this example is `scikit-learn`, often referred to as `sklearn`. It is a widely-used machine learning library in Python.