sklearn.linear_model.lasso_stability_path

sklearn.linear_model.lasso_stability_path(X, y, scaling=0.5, random_state=None, n_resampling=200, n_grid=100, sample_fraction=0.75, eps=8.881784197001252e16, n_jobs=None, verbose=False)
[source]

DEPRECATED: The function lasso_stability_path is deprecated in 0.19 and will be removed in 0.21.
Stability path based on randomized Lasso estimates
Parameters: 

X : arraylike, shape = [n_samples, n_features] 
training data. 
y : arraylike, shape = [n_samples] 
target values. 
scaling : float, optional, default=0.5 
The alpha parameter in the stability selection article used to randomly scale the features. Should be between 0 and 1. 
random_state : int, RandomState instance or None, optional, default=None 
The generator used to randomize the design. If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random . 
n_resampling : int, optional, default=200 
Number of randomized models. 
n_grid : int, optional, default=100 
Number of grid points. The path is linearly reinterpolated on a grid between 0 and 1 before computing the scores. 
sample_fraction : float, optional, default=0.75 
The fraction of samples to be used in each randomized design. Should be between 0 and 1. If 1, all samples are used. 
eps : float, optional 
Smallest value of alpha / alpha_max considered 
n_jobs : int or None, optional (default=None) 
Number of CPUs to use during the resampling. None means 1 unless in a joblib.parallel_backend context. 1 means using all processors. See Glossary for more details. 
verbose : boolean or integer, optional 
Sets the verbosity amount 
Returns: 

alphas_grid : array, shape ~ [n_grid] 
The grid points between 0 and 1: alpha/alpha_max 
scores_path : array, shape = [n_features, n_grid] 
The scores for each feature along the path. 