6.2.12. scikits.learn.linear_model.lasso_path¶
- scikits.learn.linear_model.lasso_path(X, y, eps=0.001, n_alphas=100, alphas=None, fit_intercept=True, verbose=False, **fit_params)¶
Compute Lasso path with coordinate descent
Parameters : X : numpy array of shape [n_samples,n_features]
Training data. Pass directly as fortran contiguous data to avoid unnecessary memory duplication
y : numpy array of shape [n_samples]
Target values
eps : float, optional
Length of the path. eps=1e-3 means that alpha_min / alpha_max = 1e-3
n_alphas : int, optional
Number of alphas along the regularization path
alphas : numpy array, optional
List of alphas where to compute the models. If None alphas are set automatically
fit_params : kwargs
keyword arguments passed to the Lasso fit method
Returns : models : a list of models along the regularization path
Notes
See examples/plot_lasso_coordinate_descent_path.py for an example.
To avoid unnecessary memory duplication the X argument of the fit method should be directly passed as a fortran contiguous numpy array.