scikits.learn.linear_model.LARS

class scikits.learn.linear_model.LARS(fit_intercept=True, verbose=False)

Least Angle Regression model a.k.a. LAR

Parameters :

n_features : int, optional

Number of selected active features

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).

See also

lars_path, LassoLARS

References

http://en.wikipedia.org/wiki/Least_angle_regression

Examples

>>> from scikits.learn import linear_model
>>> clf = linear_model.LARS()
>>> clf.fit([[-1,1], [0, 0], [1, 1]], [-1, 0, -1], max_features=1)
LARS(verbose=False, fit_intercept=True)
>>> print clf.coef_
[ 0.         -0.81649658]

Attributes

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

Methods

__init__(fit_intercept=True, verbose=False)

fit(X, y, normalize=True, max_features=None, precompute='auto', overwrite_X=False, **params)

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.

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.

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