scikits.learn.linear_model.ElasticNet

class scikits.learn.linear_model.ElasticNet(alpha=1.0, rho=0.5, fit_intercept=True)

Linear Model trained with L1 and L2 prior as regularizer

rho=1 is the lasso penalty. Currently, rho <= 0.01 is not reliable, unless you supply your own sequence of alpha.

Parameters :

alpha : float

Constant that multiplies the L1 term. Defaults to 1.0

rho : float

The ElasticNet mixing parameter, with 0 < rho <= 1.

coef_: ndarray of shape n_features :

The initial coeffients to warm-start the optimization

fit_intercept: bool :

Whether the intercept should be estimated or not. If False, the data is assumed to be already centered.

Notes

To avoid unnecessary memory duplication the X argument of the fit method should be directly passed as a fortran contiguous numpy array.

Methods

__init__(alpha=1.0, rho=0.5, fit_intercept=True)

fit(X, y, precompute='auto', Xy=None, max_iter=1000, tol=0.0001, coef_init=None, **params)

Fit Elastic Net model with coordinate descent

Parameters :

X: ndarray, (n_samples, n_features) :

Data

y: ndarray, (n_samples) :

Target

precompute : True | False | ‘auto’ | array-like

Whether to use a precomputed Gram matrix to speed up calculations. If set to ‘auto’ let us decide. The Gram matrix can also be passed as argument.

Xy : array-like, optional

Xy = np.dot(X.T, y) that can be precomputed. It is useful only when the Gram matrix is precomuted.

max_iter: int, optional :

The maximum number of iterations

tol: float, optional :

The tolerance for the optimization: if the updates are smaller than ‘tol’, the optimization code checks the dual gap for optimality and continues until it is smaller than tol.

Notes

Coordinate descent is an algorithm that considers each column of data at a time hence it will automatically convert the X input as a fortran contiguous numpy array if necessary.

To avoid memory re-allocation it is advised to allocate the initial data in memory directly using that format.

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