scikits.learn.glm.Lasso

class scikits.learn.glm.Lasso(alpha=1.0, fit_intercept=True, coef_=None)

Linear Model trained with L1 prior as regularizer (aka the Lasso)

Technically the Lasso model is optimizing the same objective function as the Elastic Net with rho=1.0 (no L2 penalty).

Parameters :

alpha : float, optional

Constant that multiplies the L1 term. Defaults to 1.0

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).

Notes

The algorithm used to fit the model is coordinate descent.

Examples

>>> from scikits.learn import glm
>>> clf = glm.Lasso(alpha=0.1)
>>> clf.fit([[0,0], [1, 1], [2, 2]], [0, 1, 2])
Lasso(alpha=0.1, coef_=array([ 0.85,  0.  ]), fit_intercept=True)
>>> print clf.coef_
[ 0.85  0.  ]
>>> print clf.intercept_
0.15

Attributes

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

Methods

fit
predict
score

__init__(alpha=1.0, fit_intercept=True, coef_=None)

fit(X, Y, maxit=1000, tol=0.0001, **params)

Fit Elastic Net model with coordinate descent

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 explained variance of the prediction

Parameters :

X : array-like, shape = [n_samples, n_features]

Training set.

y : array-like, shape = [n_samples]

Returns :

z : float