class sklearn.linear_model.Lars(fit_intercept=True, verbose=False, normalize=True, precompute='auto', n_nonzero_coefs=500, eps=2.2204460492503131e-16, copy_X=True, fit_path=True)[source]

Least Angle Regression model a.k.a. LAR


n_nonzero_coefs : int, optional

Target number of non-zero coefficients. Use np.inf for no limit.

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).

verbose : boolean or integer, optional

Sets the verbosity amount

normalize : boolean, optional, default False

If True, the regressors X will be normalized before regression.

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.

copy_X : boolean, optional, default True

If True, X will be copied; else, it may be overwritten.

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 n_nonzero_coefs or n_features, 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) | list 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.


>>> from sklearn import linear_model
>>> clf = linear_model.Lars(n_nonzero_coefs=1)
>>>[[-1, 1], [0, 0], [1, 1]], [-1.1111, 0, -1.1111])
Lars(copy_X=True, eps=..., fit_intercept=True, fit_path=True,
   n_nonzero_coefs=1, normalize=True, precompute='auto', verbose=False)
>>> print(clf.coef_) 
[ 0. -1.11...]


__init__(fit_intercept=True, verbose=False, normalize=True, precompute='auto', n_nonzero_coefs=500, eps=2.2204460492503131e-16, copy_X=True, fit_path=True)[source]

Decision function of the linear model.


X : {array-like, sparse matrix}, shape = (n_samples, n_features)



C : array, shape = (n_samples,)

Returns predicted values.

fit(X, y, Xy=None)[source]

Fit the model using X, y as training data.


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 =, y) that can be precomputed. It is useful only when the Gram matrix is precomputed.


self : object

returns an instance of self.


Get parameters for this estimator.


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 using the linear model


X : {array-like, sparse matrix}, shape = (n_samples, n_features)



C : array, shape = (n_samples,)

Returns predicted values.

score(X, y, sample_weight=None)[source]

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, lower values are worse.


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 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 :