Linear models are simpler. GBMs are more powerful, more flexible, and faster.
Every ML course I took had 3 weeks of problem sets on VC dimension and convex quadratic optimization in Lagrangian dual-space, while decision tree ensembles were lucky to get a mention. Meanwhile GBMs continue to win almost all the competitions where neural nets don't dominate.
I suspect my professors just preferred the nice theoretical motivation and fancy math.
Linear models are simpler. GBMs are more powerful, more flexible, and faster.
Every ML course I took had 3 weeks of problem sets on VC dimension and convex quadratic optimization in Lagrangian dual-space, while decision tree ensembles were lucky to get a mention. Meanwhile GBMs continue to win almost all the competitions where neural nets don't dominate.
I suspect my professors just preferred the nice theoretical motivation and fancy math.