9.2.12. sklearn.linear_model.LassoLarsIC

class sklearn.linear_model.LassoLarsIC(criterion='aic', fit_intercept=True, verbose=False, normalize=True, precompute='auto', max_iter=500, eps=2.2204460492503131e-16, overwrite_X=False)

Lasso model fit with Lars using BIC or AIC for model selection

AIC is the Akaike information criterion and BIC is the Bayes Information criterion. Such criteria are useful to select the value of the regularization parameter by making a trade-off between the goodness of fit and the complexity of the model. A good model should explain well the data while being simple.

Parameters :

criterion: ‘bic’ | ‘aic’ :

The type of criterion to use.

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

overwrite_X : boolean, optional

Default is False. If True, X will 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. Can be used for early stopping.

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.

See also

lars_path, LassoLars, LassoLarsCV

References

The estimation of the number of degrees of freedom is given by:

“On the degrees of freedom of the lasso” Hui Zou, Trevor Hastie, and Robert Tibshirani Ann. Statist. Volume 35, Number 5 (2007), 2173-2192.

http://en.wikipedia.org/wiki/Akaike_information_criterion http://en.wikipedia.org/wiki/Bayesian_information_criterion

Examples

>>> from sklearn import linear_model
>>> clf = linear_model.LassoLarsIC(criterion='bic')
>>> clf.fit([[-1, 1], [0, 0], [1, 1]], [-1.1111, 0, -1.1111]) 
LassoLarsIC(criterion='bic', eps=..., fit_intercept=True,
      max_iter=500, normalize=True, overwrite_X=False, precompute='auto',
      verbose=False)
>>> print clf.coef_ 
[ 0.  -1.11...]

Attributes

coef_	array, shape = [n_features]	parameter vector (w in the fomulation formula)
intercept_	float	independent term in decision function.

Methods

fit(X, y[, overwrite_X])	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__(criterion='aic', fit_intercept=True, verbose=False, normalize=True, precompute='auto', max_iter=500, eps=2.2204460492503131e-16, overwrite_X=False)

fit(X, y, overwrite_X=False)

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 :