My introduction to autoML was MuMIn, an R package that didn't pitch itself as autoML, but rather pitched [generalized] linear model selection as an information theoretics problem. AutoML at its best, for me, remains an algorithm-agnostic solution to an information theoretics problem.
I recapitulated this history in grad school down to most of the tools—though i did use JMP for a semester before returning to SAS and I didn’t write any fortran until I started using R.
When i first learned neuralnets it was 2002 and my stats professor cautioned me: Neural networks are what really smart people who believe magic end up sacrificing their careers for. After a summer dalliance I got back to work with glm and some agent based modeling (ecology).
My introduction to autoML was MuMIn, an R package that didn't pitch itself as autoML, but rather pitched [generalized] linear model selection as an information theoretics problem. AutoML at its best, for me, remains an algorithm-agnostic solution to an information theoretics problem.
Found here, for anyone who wants to check it out
https://cran.r-project.org/web/packages/MuMIn/index.html
I recapitulated this history in grad school down to most of the tools—though i did use JMP for a semester before returning to SAS and I didn’t write any fortran until I started using R.
When i first learned neuralnets it was 2002 and my stats professor cautioned me: Neural networks are what really smart people who believe magic end up sacrificing their careers for. After a summer dalliance I got back to work with glm and some agent based modeling (ecology).
Don’t be them, he said. Alas, I wasn’t them.
Thanks for reading and commenting!