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
--------
orthogonal_mp : Solves n_targets Orthogonal Matching Pursuit problems.
orthogonal_mp_gram : Solves n_targets Orthogonal Matching Pursuit
    problems using only the Gram matrix X.T * X and the product X.T * y.
lars_path : Compute Least Angle Regression or Lasso path using LARS algorithm.
Lars : Least Angle Regression model a.k.a. LAR.
LassoLars : Lasso model fit with Least Angle Regression a.k.a. Lars.
OrthogonalMatchingPursuit : Orthogonal Matching Pursuit model (OMP).
LarsCV : Cross-validated Least Angle Regression model.
LassoLarsCV : Cross-validated Lasso model fit with Least Angle Regression.
sklearn.decomposition.sparse_encode : Generic sparse coding.
    Each column of the result is the solution to a Lasso problem.

Notes
-----
In `fit`, once the optimal number of non-zero coefficients is found through
cross-validation, the model is fit again using the entire training set.

Examples
--------
>>> from sklearn.linear_model import OrthogonalMatchingPursuitCV
>>> from sklearn.datasets import make_regression
>>> X, y = make_regression(n_features=100, n_informative=10,
...                        noise=4, random_state=0)
>>> reg = OrthogonalMatchingPursuitCV(cv=5).fit(X, y)
>>> reg.score(X, y)
0.9991...
>>> reg.n_nonzero_coefs_
np.int64(10)
>>> reg.predict(X[:1,])
array([-78.3854...])
rl