Followed by Leo machine learning sklearn-Kernel ridge regression

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1.3. Kernel ridge regression

from sklearn.kernel_ridge import KernelRidge
import numpy as np
n_samples, n_features = 10, 5
rng = np.random.RandomState(0)
y = rng.randn(n_samples)
X = rng.randn(n_samples, n_features)
clf = KernelRidge(alpha=1.0)
clf.fit(X, y)

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