Thanks Vlad,
Can you tell me how to take care of that?
On Sat, Feb 15, 2014 at 2:10 PM, Vlad Niculae <[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]>
> 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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>
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> Get your Android app in front of a whole new audience. Start now.
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--
Regards,
Manoj Kumar,
Mech Undergrad
http://manojbits.wordpress.com
------------------------------------------------------------------------------
Android apps run on BlackBerry 10
Introducing the new BlackBerry 10.2.1 Runtime for Android apps.
Now with support for Jelly Bean, Bluetooth, Mapview and more.
Get your Android app in front of a whole new audience. Start now.
http://pubads.g.doubleclick.net/gampad/clk?id=124407151&iu=/4140/ostg.clktrk
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