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=8.8817841970012523e-16, n_jobs=1, verbose=False)

Stabiliy path based on randomized Lasso estimates

Parameters:

X : array-like, shape = [n_samples, n_features]

training data.

y : array-like, 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 : integer or numpy.random.RandomState, optional

The generator used to randomize the design.

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 : integer, optional

Number of CPUs to use during the resampling. If ‘-1’, use all the CPUs

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.

Notes

See examples/linear_model/plot_sparse_recovery.py for an example.