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The guess when Dru hits 20,000 posts thread


Loose_Brie

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One could fit least squares polynomials in Matlab by solving A^T*Ax = A^T*b where A is an m x n matrix of m explanatatory variables of degree n , A^T is the transpose of A, b is an n-vector of y values, and x is an m-vector of coefficients (the least squares coefficients). Geek_em8.gifGeek_em8.gifGeek_em8.gifGeek_em8.gifGeek_em8.gifGeek_em8.gif

 

A real matlab user would just say A\b.

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Using a 6th-degree polynomial to model that curve is lame. The fit is snug because it's overfit, and there's absolutely no justification for why the high-degree poly model is appropriate.

 

Prove it.

Geek_em8.gifGeek_em8.gifGeek_em8.gifGeek_em8.gif

 

Take the first half of data. Build a 6th-degree poly fitting it. Extrapolate the data and see how the predictions compare to the second half of the data. Notice how the predictions suck. QEthe_finger.gifD

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Using a 6th-degree polynomial to model that curve is lame. The fit is snug because it's overfit, and there's absolutely no justification for why the high-degree poly model is appropriate.

 

Prove it.

Geek_em8.gifGeek_em8.gifGeek_em8.gifGeek_em8.gif

 

Take the first half of data. Build a 6th-degree poly fitting it. Extrapolate the data and see how the predictions compare to the second half of the data. Notice how the predictions suck. QEthe_finger.gifD

 

ok fine, you get 10 Geek_em8.gif points for your post. But you lose 9 of them for ignoring that the 6th-deg poly fit was just a joke!! Well you get them back if you do a periodic (sin/cos function) fit on {Dru} because it will probably be the most accurate prediction!! Geek_em8.giffruit.gif

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