sklearn.linear_model.orthogonal_mp()

X : array, shape (n_samples, n_features)

Input data. Columns are assumed to have unit norm.

y : array, shape (n_samples,) or (n_samples, n_targets)

Input targets

n_nonzero_coefs : int

Desired number of non-zero entries in the solution. If None (by default) this value is set to 10% of n_features.

tol : float

Maximum norm of the residual. If not None, overrides n_nonzero_coefs.

precompute : {True, False, ?auto?},

Whether to perform precomputations. Improves performance when n_targets or n_samples is very large.

copy_X : bool, optional

Whether the design matrix X must be copied by the algorithm. A false value is only helpful if X is already Fortran-ordered, otherwise a copy is made anyway.

return_path : bool, optional. Default: False

Whether to return every value of the nonzero coefficients along the forward path. Useful for cross-validation.

return_n_iter : bool, optional default False

Whether or not to return the number of iterations.