When A Ridge Regression Maxi Meets Random Forest
Chief Ridge Officer strikes back
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Friends, welcome back.
A lot of Ridge haters out there have been rejoicing after reading that I'm looking into random forest and tree models. As the people's Chief Ridge Officer (Lisan Al-Gaib), it's my utmost duty to teach these Harkonnens a lesson.
should I embark on a journey of studying tree models (Random Forest/XGB/LightGBM wtv) *only* for 30 days…… can’t say I’m excited about it tho. but I think I’m not excited bcs I don’t even know enough about it….
— quantymacro (@quantymacro) May 14, 2024
First we will show that Random Forest is nothing more than a copycat. Yes, it's true. There is no crime worse than stealing. Especially tweets and jokes. Or regularization technique. The OG is actually Ridge Regression.
And then with our knowledge of Ridge Regression, we will try to enhance by Random Forest by a performing a trick. Or we can colloquially call it Random Ridge Forest.
back from vacation my friends. been reading about Random Forest, and damn there are *so many* interesting tricks to get the most out of RF. and all of it require hacking the underlying model. friends don’t let friends import sklearn
— quantymacro (@quantymacro) June 3, 2024
This article will be a dense one. I tried my best to skip all the non-essential stuffs and only get to the main points, but as we all know the devil is in the details. And I'm on a journey to understand tree models on a deep level, and I wouldn't do my audience a disservice by robbing them from the opportunity to tag along. So get your coffee, fix your posture, and let's do this.