9.2.23.1. sklearn.linear_model.sparse.Lasso

class sklearn.linear_model.sparse.Lasso(alpha=1.0, fit_intercept=False, normalize=False, max_iter=1000, tol=0.0001)

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)	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, 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 :