Fixed effects on machine learning models? I have been using fixed effects (from fixest package) on different type of panel data (longitudinal) models. Mainly parametric.
I was wondering if fixed effects could be apply to machine learning models like random forest?
(My background is not in stat/econometrics)

Fixed vs random effects is a terminology used to describe different kinds of regression models (linear regression, generalized linear regression, etc). If you want to understand it better check threads like What is the difference between fixed effect, random effect and mixed effect models? and What is a difference between random effects-, fixed effects- and marginal model?. The terminology regards how we treat, interpret, and estimate the parameters of the models. In the case of machine learning models, some are non-parametric, e.g. $k$NN, decision trees, some technically do have parameters, but they play a different role as compared to standard regression models. So there isn't something like fixed vs random effects for machine learning models. Also, notice that the distinction is not that sharp, which is why some authors (e.g. Gelman and Hill) avoid using it alltogether.
