scikits.learn.glm.ElasticNet¶
- class scikits.learn.glm.ElasticNet(alpha=1.0, rho=0.5, coef_=None, 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.
Methods
fit predict score - __init__(alpha=1.0, rho=0.5, coef_=None, fit_intercept=True)¶
- fit(X, Y, maxit=1000, tol=0.0001, **params)¶
Fit Elastic Net model with coordinate descent
- 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