8.14.1.10. sklearn.linear_model.LarsCV

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

Cross-validated Least Angle Regression model

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

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.

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.

See also

lars_path, LassoLARS, LassoLarsCV

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

Methods

decision_function(X)	Decision function of the linear model
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 R^2 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, copy_X=True)

decision_function(X)

Decision function of the linear model

Parameters :

X : numpy array of shape [n_samples, n_features]

Returns :

C : array, shape = [n_samples]

Returns predicted values.

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 R^2 of the prediction.

The coefficient R^2 is defined as (1 - u/v), where u is the regression sum of squares ((y - 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.

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 :