# `sklearn.linear_model`.LassoLars¶

class `sklearn.linear_model.``LassoLars`(alpha=1.0, fit_intercept=True, verbose=False, normalize=True, precompute='auto', max_iter=500, eps=2.2204460492503131e-16, copy_X=True, fit_path=True, positive=False)[源代码]

Lasso model fit with Least Angle Regression a.k.a. Lars

It is a Linear Model trained with an L1 prior as regularizer.

The optimization objective for Lasso is:

```(1 / (2 * n_samples)) * ||y - Xw||^2_2 + alpha * ||w||_1
```

Read more in the User Guide.

Parameters: alpha : float Constant that multiplies the penalty term. Defaults to 1.0. `alpha = 0` is equivalent to an ordinary least square, solved by `LinearRegression`. For numerical reasons, using `alpha = 0` with the LassoLars object is not advised and you should prefer the LinearRegression object. fit_intercept : boolean whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (e.g. data is expected to be already centered). positive : boolean (default=False) Restrict coefficients to be >= 0. Be aware that you might want to remove fit_intercept which is set True by default. Under the positive restriction the model coefficients will not converge to the ordinary-least-squares solution for small values of alpha. Only coeffiencts up to the smallest alpha value (```alphas_[alphas_ > 0.].min()``` when fit_path=True) reached by the stepwise Lars-Lasso algorithm are typically in congruence with the solution of the coordinate descent Lasso estimator. verbose : boolean or integer, optional Sets the verbosity amount normalize : boolean, optional, default False If True, the regressors X will be normalized before regression. copy_X : boolean, optional, default True If True, X will be copied; else, it may be overwritten. 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. max_iter : integer, optional Maximum number of iterations to perform. eps : float, optional The machine-precision regularization in the computation of the Cholesky diagonal factors. Increase this for very ill-conditioned systems. Unlike the `tol` parameter in some iterative optimization-based algorithms, this parameter does not control the tolerance of the optimization. fit_path : boolean If `True` the full path is stored in the `coef_path_` attribute. If you compute the solution for a large problem or many targets, setting `fit_path` to `False` will lead to a speedup, especially with a small alpha. alphas_ : array, shape (n_alphas + 1,) | list of n_targets such arrays Maximum of covariances (in absolute value) at each iteration. `n_alphas` is either `max_iter`, `n_features`, or the number of nodes in the path with correlation greater than `alpha`, whichever is smaller. active_ : list, length = n_alphas | list of n_targets such lists Indices of active variables at the end of the path. coef_path_ : array, shape (n_features, n_alphas + 1) or list If a list is passed it’s expected to be one of n_targets such arrays. The varying values of the coefficients along the path. It is not present if the `fit_path` parameter is `False`. coef_ : array, shape (n_features,) or (n_targets, n_features) Parameter vector (w in the formulation formula). intercept_ : float | array, shape (n_targets,) Independent term in decision function. n_iter_ : array-like or int. The number of iterations taken by lars_path to find the grid of alphas for each target.

Examples

```>>> from sklearn import linear_model
>>> clf = linear_model.LassoLars(alpha=0.01)
>>> clf.fit([[-1, 1], [0, 0], [1, 1]], [-1, 0, -1])
...
LassoLars(alpha=0.01, copy_X=True, eps=..., fit_intercept=True,
fit_path=True, max_iter=500, normalize=True, positive=False,
precompute='auto', verbose=False)
>>> print(clf.coef_)
[ 0.         -0.963257...]
```

Methods

 `decision_function`(*args, **kwargs) DEPRECATED: and will be removed in 0.19. `fit`(X, y[, Xy]) Fit the model using X, y as training data. `get_params`([deep]) Get parameters for this estimator. `predict`(X) Predict using the linear model `score`(X, y[, sample_weight]) Returns the coefficient of determination R^2 of the prediction. `set_params`(**params) Set the parameters of this estimator.
`__init__`(alpha=1.0, fit_intercept=True, verbose=False, normalize=True, precompute='auto', max_iter=500, eps=2.2204460492503131e-16, copy_X=True, fit_path=True, positive=False)[源代码]
`decision_function`(*args, **kwargs)[源代码]

DEPRECATED: and will be removed in 0.19.

Decision function of the linear model.

Parameters: X : {array-like, sparse matrix}, shape = (n_samples, n_features) Samples. C : array, shape = (n_samples,) Returns predicted values.
`fit`(X, y, Xy=None)[源代码]

Fit the model using X, y as training data.

Parameters: X : array-like, shape (n_samples, n_features) Training data. y : array-like, shape (n_samples,) or (n_samples, n_targets) Target values. Xy : array-like, shape (n_samples,) or (n_samples, n_targets), optional Xy = np.dot(X.T, y) that can be precomputed. It is useful only when the Gram matrix is precomputed. self : object returns an instance of self.
`get_params`(deep=True)[源代码]

Get parameters for this estimator.

Parameters: deep: boolean, optional : If True, will return the parameters for this estimator and contained subobjects that are estimators. params : mapping of string to any Parameter names mapped to their values.
`predict`(X)[源代码]

Predict using the linear model

Parameters: X : {array-like, sparse matrix}, shape = (n_samples, n_features) Samples. C : array, shape = (n_samples,) Returns predicted values.
`score`(X, y, sample_weight=None)[源代码]

Returns the coefficient of determination R^2 of the prediction.

The coefficient R^2 is defined as (1 - u/v), where u is the regression sum of squares ((y_true - y_pred) ** 2).sum() and v is the residual sum of squares ((y_true - y_true.mean()) ** 2).sum(). Best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a R^2 score of 0.0.

Parameters: X : array-like, shape = (n_samples, n_features) Test samples. y : array-like, shape = (n_samples) or (n_samples, n_outputs) True values for X. sample_weight : array-like, shape = [n_samples], optional Sample weights. score : float R^2 of self.predict(X) wrt. y.
`set_params`(**params)[源代码]

Set the parameters of this 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 :