Scikit-learn Ridge Regression with Irregular Intercept
Does the ridge regression using a skikit that includes an intercept coefficient in the regularization term, and if so, is there a way to run the ridge regression without intercept regularization?
Suppose I agree with ridge regression:
from sklearn import linear_model mymodel = linear_model.Ridge(alpha=0.1, fit_intercept=True).fit(X, y) print mymodel.coef_ print mymodel.intercept_
for some data X, y, where X does not contain a column of 1. fit_intercept = True will automatically add an intercept column, and the corresponding coefficient will be specified by mymodel.intercept_. I can't figure out if this intercept ratio is part of the regularization summation in the optimization goal.
According to http://scikit-learn.org/stable/modules/linear_model.html the optimization goal is to minimize with respect to w:
|| X * w - y || ** 2 + alpha * || w || ** 2
(using the L2 norm). The second term is a regularization term, and the question is whether it includes the intercept factor when we set fit_intercept = True; and if so how to disable it.
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Interception is not penalized. Just try a simple 3 point example with a lot of interception.
from sklearn import linear_model import numpy as np x=np.array([-1,0,1]).reshape((3,1)) y=np.array([1001,1002,1003]) fit=linear_model.Ridge(alpha=0.1,fit_intercept=True).fit(x,y) print fit.intercept_ print fit.coef_
Intercept was set to intercept MLE (1002) and tilt was penalized (0.952 instead of 1).
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