9.2.11. sklearn.linear_model.LassoLarsCV

class sklearn.linear_model.LassoLarsCV(fit_intercept=True, verbose=False, max_iter=500, normalize=True, precompute='auto', cv=None, n_jobs=1, eps=2.2204460492503131e-16, overwrite_X=False)

Cross-validated Lasso, using the LARS algorithm

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

normalize : boolean, optional

If True, the regressors X are normalized

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.

cv : crossvalidation generator, optional

see sklearn.cross_validation module. If None is passed, default to a 5-fold strategy

n_jobs : integer, optional

Number of CPUs to use during the cross validation. If ‘-1’, use all the CPUs

eps: float, optional :

The machine-precision regularization in the computation of the Cholesky diagonal factors. Increase this for very ill-conditioned systems.

overwrite_X : boolean, optional

If True, X will not be copied Default is False

See also

lars_path, LassoLARS, LarsCV, LassoCV

Notes

The object solves the same problem as the LassoCV object. However, unlike the LassoCV, it find the relevent 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.

Attributes

coef_	array, shape = [n_features]	parameter vector (w in the fomulation formula)
intercept_	float	independent term in decision function.
coef_path: array, shape = [n_features, n_alpha]		the varying values of the coefficients along the path
alphas_: array, shape = [n_alpha]		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
cv_mse_path_: array, shape = [n_folds, n_cv_alphas]		the mean square error on left-out for each fold along the path (alpha values given by cv_alphas)

Methods

fit(X, y)	Fit the model using X, y as training data.
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__(fit_intercept=True, verbose=False, max_iter=500, normalize=True, precompute='auto', cv=None, n_jobs=1, eps=2.2204460492503131e-16, overwrite_X=False)

fit(X, y)

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]

Target values.

Returns :

self : object

returns an instance of self.

predict(X)

Predict using the linear model

Parameters :

X : numpy array of shape [n_samples, n_features]

Returns :

C : array, shape = [n_samples]

Returns predicted 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 :