6.2.14.1. scikits.learn.linear_model.sparse.Lasso¶
- class scikits.learn.linear_model.sparse.Lasso(alpha=1.0, fit_intercept=False)¶
Linear Model trained with L1 prior as regularizer
This implementation works on scipy.sparse X and dense coef_. Technically this is the same as Elastic Net with the L2 penalty set to zero.
Parameters : alpha : float
Constant that multiplies the L1 term. Defaults to 1.0
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(X, y[, max_iter, tol]) Fit current model with coordinate descent predict(X) Predict using the linear model score(X, y) Returns the coefficient of determination of the prediction - __init__(alpha=1.0, fit_intercept=False)¶
- fit(X, y, max_iter=1000, tol=0.0001, **params)¶
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