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[D] 2nd Order Approximation in XGboost’s Objective Function

Hi all,

I have a quick question regarding XGboost’s objective function. I was reading the XGboost paper (https://arxiv.org/pdf/1603.02754.pdf). I see that authors approximated the original objective function using a 2nd order Taylor series (page 2, section 2.2). Is there a particular reason why it’s expanded to 2nd degree and not higher? I’m guessing that linear apprx. is not enough and higher orders require more computational power, but is there a mathematical background or is this a design choice?

submitted by /u/_kty
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.