This talks about tensorflow and I’ve been looking at scikit’s random forest regression.
I have about one million rows of tabular data, with 15 features, to make price predictions.
Is there a definitively better choice between the two?
I have about one million rows of tabular data, with 15 features, to make price predictions.
Is there a definitively better choice between the two?
XGBoost is the og and the most feature-rich.
LightGBM is the fastest and what I use for my case (millions of rows of data with over 100 features).
CatBoost could be good depending on the nature of your data, for example if you have a lot of categorical types.
EDIT: Alos, they all support GPU training, but I haven't been able to make that faster than just using more CPU cores.
All three are lightyears ahead of naive random forest implementations, and are in very active development.