Crossvalidated Lasso, using the LARS algorithm
The optimization objective for Lasso is:
(1 / (2 * n_samples)) * y  Xw^2_2 + alpha * w_1
Read more in the User Guide.
Parameters: 

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 
max_iter : integer, optional 
Maximum number of iterations to perform. 
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’ 
Whether to use a precomputed Gram matrix to speed up calculations. If set to 'auto' let us decide. The Gram matrix cannot be passed as argument since we will use only subsets of X. 
cv : int, crossvalidation generator or an iterable, optional 
Determines the crossvalidation splitting strategy. Possible inputs for cv are:  None, to use the default 3fold crossvalidation,
 integer, to specify the number of folds.
 An object to be used as a crossvalidation generator.
 An iterable yielding train/test splits.
For integer/None inputs, KFold is used. Refer User Guide for the various crossvalidation strategies that can be used here. Changed in version 0.20: cv default value if None will change from 3fold to 5fold in v0.22. 
max_n_alphas : integer, optional 
The maximum number of points on the path used to compute the residuals in the crossvalidation 
n_jobs : int or None, optional (default=None) 
Number of CPUs to use during the cross validation. None means 1 unless in a joblib.parallel_backend context. 1 means using all processors. See Glossary for more details. 
eps : float, optional 
The machineprecision regularization in the computation of the Cholesky diagonal factors. Increase this for very illconditioned systems. 
copy_X : boolean, optional, default True 
If True, X will be copied; else, it may be overwritten. 
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 do 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. As a consequence using LassoLarsCV only makes sense for problems where a sparse solution is expected and/or reached. 
Attributes: 

coef_ : array, shape (n_features,) 
parameter vector (w in the formulation formula) 
intercept_ : float 
independent term in decision function. 
coef_path_ : array, shape (n_features, n_alphas) 
the varying values of the coefficients along the path 
alpha_ : float 
the estimated regularization parameter alpha 
alphas_ : array, shape (n_alphas,) 
the different values of alpha along the path 
cv_alphas_ : array, shape (n_cv_alphas,) 
all the values of alpha along the path for the different folds 
mse_path_ : array, shape (n_folds, n_cv_alphas) 
the mean square error on leftout for each fold along the path (alpha values given by cv_alphas ) 
n_iter_ : arraylike or int 
the number of iterations run by Lars with the optimal alpha. 
Notes
The object solves the same problem as the LassoCV object. However, unlike the LassoCV, it find the relevant alphas values by itself. In general, because of this property, it will be more stable. However, it is more fragile to heavily multicollinear datasets.
It is more efficient than the LassoCV if only a small number of features are selected compared to the total number, for instance if there are very few samples compared to the number of features.
Examples
>>> from sklearn.linear_model import LassoLarsCV
>>> from sklearn.datasets import make_regression
>>> X, y = make_regression(noise=4.0, random_state=0)
>>> reg = LassoLarsCV(cv=5).fit(X, y)
>>> reg.score(X, y)
0.9992...
>>> reg.alpha_
0.0484...
>>> reg.predict(X[:1,])
array([77.8723...])
Methods
fit (X, y)  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__(fit_intercept=True, verbose=False, max_iter=500, normalize=True, precompute=’auto’, cv=’warn’, max_n_alphas=1000, n_jobs=None, eps=2.220446049250313e16, copy_X=True, positive=False)
[source]

alpha

DEPRECATED: Attribute alpha is deprecated in 0.19 and will be removed in 0.21. See alpha_
instead

fit(X, y)
[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,) 
Target values. 
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.