Hello
I have a query with fit_intercept parameter in most of the estimators.
When we have a linear model like w0 + w1*x1 + w2*x2 + .. I'm assuming that
clf.intercept_ takes care of the w0 term since the data is centered.
Then why aren't these two equivalent?
X = np.random.rand(100, 10)
y = np.random.rand(100)
X1 = np.ones((100, 11))
X1[:, 1:] = X
clf = Ridge(fit_intercept=True)
clf.fit(X, y)
print clf.intercept_ + np.dot(X, clf.coef_)
clf1 = Ridge(fit_intercept=False)
clf1.fit(X1, y)
print np.dot(X1, clf1.coef_)
I get some minor differences. Hope someone can enlighten me as usual.
--
Regards,
Manoj Kumar,
Mech Undergrad
http://manojbits.wordpress.com
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