which is also required for prediction. If
    kernel == "precomputed" this is instead the precomputed
    training matrix, of shape (n_samples, n_samples).

n_features_in_ : int
    Number of features seen during :term:`fit`.

    .. versionadded:: 0.24

feature_names_in_ : ndarray of shape (`n_features_in_`,)
    Names of features seen during :term:`fit`. Defined only when `X`
    has feature names that are all strings.

    .. versionadded:: 1.0

See Also
--------
sklearn.gaussian_process.GaussianProcessRegressor : Gaussian
    Process regressor providing automatic kernel hyperparameters
    tuning and predictions uncertainty.
sklearn.linear_model.Ridge : Linear ridge regression.
sklearn.linear_model.RidgeCV : Ridge regression with built-in
    cross-validation.
sklearn.svm.SVR : Support Vector Regression accepting a large variety
    of kernels.

References
----------
* Kevin P. Murphy
  "Machine Learning: A Probabilistic Perspective", The MIT Press
  chapter 14.4.3, pp. 492-493

Examples
--------
>>> 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)
>>> krr = KernelRidge(alpha=1.0)
>>> krr.fit(X, y)
KernelRidge(alpha=1.0)
r