9.2.23.2. sklearn.linear_model.sparse.ElasticNet¶
- class sklearn.linear_model.sparse.ElasticNet(alpha=1.0, rho=0.5, fit_intercept=False, normalize=False, max_iter=1000, tol=0.0001)¶
Linear Model trained with L1 and L2 prior as regularizer
This implementation works on scipy.sparse X and dense coef_.
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.
TODO: fit_intercept=True is not yet implemented
Notes
The parameter rho corresponds to alpha in the glmnet R package while alpha corresponds to the lambda parameter in glmnet.
Methods
fit(X, y) Fit current model with coordinate descent predict(X) Predict using the linear model score(X, y) Returns the coefficient of determination of the prediction set_params(**params) Set the parameters of the estimator. - __init__(alpha=1.0, rho=0.5, fit_intercept=False, normalize=False, max_iter=1000, tol=0.0001)¶
- fit(X, y)¶
Fit current model with coordinate descent
X is expected to be a sparse matrix. For maximum efficiency, use a sparse matrix in CSC format (scipy.sparse.csc_matrix)
- predict(X)¶
Predict using the linear model
Parameters : X : scipy.sparse matrix of shape [n_samples, n_features] Returns : array, shape = [n_samples] with the predicted real 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
- set_params(**params)¶
Set the parameters of the estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.
Returns : self :