8.14.1.20. sklearn.linear_model.lars_path¶
- sklearn.linear_model.lars_path(X, y, Xy=None, Gram=None, max_iter=500, alpha_min=0, method='lar', copy_X=True, eps=2.2204460492503131e-16, copy_Gram=True, verbose=False)¶
Compute Least Angle Regression and Lasso path
The optimization objective for Lasso is:
(1 / (2 * n_samples)) * ||y - Xw||^2_2 + alpha * ||w||_1
Parameters : X: array, shape: (n_samples, n_features) :
Input data
y: array, shape: (n_samples) :
Input targets
max_iter: integer, optional :
Maximum number of iterations to perform, set to infinity for no limit.
Gram: None, ‘auto’, array, shape: (n_features, n_features), optional :
Precomputed Gram matrix (X’ * X), if ‘auto’, the Gram matrix is precomputed from the given X, if there are more samples than features
alpha_min: float, optional :
Minimum correlation along the path. It corresponds to the regularization parameter alpha parameter in the Lasso.
method: {‘lar’, ‘lasso’} :
Specifies the returned model. Select ‘lar’ for Least Angle Regression, ‘lasso’ for the Lasso.
eps: float, optional :
The machine-precision regularization in the computation of the Cholesky diagonal factors. Increase this for very ill-conditioned systems.
Returns : alphas: array, shape: (max_features + 1,) :
Maximum of covariances (in absolute value) at each iteration.
active: array, shape (max_features,) :
Indices of active variables at the end of the path.
coefs: array, shape (n_features, max_features + 1) :
Coefficients along the path
See also
lasso_path, LassoLars, Lars, LassoLarsCV, LarsCV, sklearn.decomposition.sparse_encode
Notes