scikits.learn.glm.LassoCV

class scikits.learn.glm.LassoCV(eps=0.001, n_alphas=100, alphas=None)

Lasso linear model with iterative fitting along a regularization path

The best model is selected by cross-validation.

Parameters :

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

Notes

See examples/glm/lasso_path_with_crossvalidation.py for an example.

Methods

fit
path
predict
score

__init__(eps=0.001, n_alphas=100, alphas=None)

fit(X, y, cv=None, **fit_params)

Fit linear model with coordinate descent along decreasing alphas using cross-validation

Parameters :

X : numpy array of shape [n_samples,n_features]

Training data

Y : numpy array of shape [n_samples]

Target values

cv : cross-validation generator, optional

If None, KFold will be used.

fit_params : kwargs

keyword arguments passed to the Lasso fit method

static path(X, y, eps=0.001, n_alphas=100, alphas=None, verbose=False, fit_params={})

Compute Lasso path with coordinate descent

Parameters :

X : numpy array of shape [n_samples,n_features]

Training data

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 : dict, optional

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.

predict(X)

Predict using the linear model

Parameters :

X : numpy array of shape [n_samples, n_features]

Returns :

C : array, shape = [n_samples]

Returns predicted values.

score(X, y)

Returns the explained variance of the prediction

Parameters :

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

Training set.

y : array-like, shape = [n_samples]

Returns :

z : float