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). 
verbose : boolean or integer, optional 
Sets the verbosity amount 
normalize : boolean, optional, default True 
This parameter is ignored when fit_intercept is set to False. If True, the regressors X will be normalized before regression by subtracting the mean and dividing by the l2norm. If you wish to standardize, please use sklearn.preprocessing.StandardScaler before calling fit on an estimator with normalize=False . 
precompute : True  False  ‘auto’  arraylike 
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 machineprecision regularization in the computation of the Cholesky diagonal factors. Increase this for very illconditioned systems. Unlike the tol parameter in some iterative optimizationbased algorithms, this parameter does not control the tolerance of the optimization. 
copy_X : boolean, optional, default True 
If True, X will be copied; else, it may be overwritten. 
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. 
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 ordinaryleastsquares solution for small values of alpha. Only coefficients up to the smallest alpha value (alphas_[alphas_ >
0.].min() when fit_path=True) reached by the stepwise LarsLasso algorithm are typically in congruence with the solution of the coordinate descent Lasso estimator. 
Attributes: 

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_ : arraylike 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
>>> reg = linear_model.LassoLars(alpha=0.01)
>>> reg.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(reg.coef_)
[ 0. 0.963257...]
Methods
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=2.220446049250313e16, copy_X=True, fit_path=True, positive=False)
[source]

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

Fit the model using X, y as training data.
Parameters: 

X : arraylike, shape (n_samples, n_features) 
Training data. 
y : arraylike, shape (n_samples,) or (n_samples, n_targets) 
Target values. 
Xy : arraylike, 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. 
Returns: 

self : object 
returns an instance of self. 

get_params(deep=True)
[source]

Get parameters for this estimator.
Parameters: 

deep : boolean, optional 
If True, will return the parameters for this estimator and contained subobjects that are estimators. 
Returns: 

params : mapping of string to any 
Parameter names mapped to their values. 

predict(X)
[source]

Predict using the linear model
Parameters: 

X : array_like or sparse matrix, shape (n_samples, n_features) 
Samples. 
Returns: 

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 residual sum of squares ((y_true  y_pred) ** 2).sum() and v is the total sum of squares ((y_true  y_true.mean()) ** 2).sum(). The 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 : arraylike, shape = (n_samples, n_features) 
Test samples. For some estimators this may be a precomputed kernel matrix instead, shape = (n_samples, n_samples_fitted], where n_samples_fitted is the number of samples used in the fitting for the estimator. 
y : arraylike, shape = (n_samples) or (n_samples, n_outputs) 
True values for X. 
sample_weight : arraylike, shape = [n_samples], optional 
Sample weights. 
Returns: 

score : float 
R^2 of self.predict(X) wrt. y. 

set_params(**params)
[source]

Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter>
so that it’s possible to update each component of a nested object.