6.2.6. scikits.learn.linear_model.ElasticNetCV

class scikits.learn.linear_model.ElasticNetCV(rho=0.5, eps=0.001, n_alphas=100, alphas=None, fit_intercept=True)

Elastic Net model with iterative fitting along a regularization path

The best model is selected by cross-validation.

Parameters :

rho : float, optional

float between 0 and 1 passed to ElasticNet (scaling between l1 and l2 penalties)

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/linear_model/lasso_path_with_crossvalidation.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.

Methods

fit(X, y[, cv])	Fit linear model with coordinate descent along decreasing alphas
path(X, y[, rho, eps, n_alphas, alphas, ...])	Compute Elastic-Net path with coordinate descent
predict(X)	Predict using the linear model
score(X, y)	Returns the coefficient of determination of the prediction

__init__(rho=0.5, eps=0.001, n_alphas=100, alphas=None, fit_intercept=True)

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. Pass directly as fortran contiguous data to avoid unnecessary memory duplication

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, rho=0.5, eps=0.001, n_alphas=100, alphas=None, fit_intercept=True, verbose=False, **fit_params)

Compute Elastic-Net 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

rho : float, optional

float between 0 and 1 passed to ElasticNet (scaling between l1 and l2 penalties). rho=1 corresponds to the Lasso

eps : float

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.

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 coefficient of determination of the prediction

Parameters :

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

Training set.

y : array-like, shape = [n_samples]

Returns :

z : float