On 15/2/2014 10:17 , Manoj Kumar wrote:
> Thanks Vlad,
>
> Can you tell me how to take care of that?


What exactly do you mean by "take care of that"?
Like Mathieu said, it probably doesn't matter too much in terms of the 
final score.

Vlad

>
> On Sat, Feb 15, 2014 at 2:10 PM, Vlad Niculae <[email protected]
> <mailto:[email protected]>> wrote:
>
>     Hi Manoj,
>
>     In the first example, the intercept is not regularized, hence the
>     difference.
>
>     Vlad
>
>     On Feb 15, 2014 8:54 AM, "Manoj Kumar"
>     <[email protected]
>     <mailto:[email protected]>> wrote:
>
>         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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> --
> Regards,
> Manoj Kumar,
> Mech Undergrad
> http://manojbits.wordpress.com
>
>
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> Android apps run on BlackBerry 10
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Get your Android app in front of a whole new audience.  Start now.
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