Learning on tabular data
2020-11-30 — 2021-06-21
Wherein tabular data is presented as the common substrate for models, and gradient boosting machines are recommended as the practical recourse, while neural PFNs are noted for recent in‑context feats.
Learning for tabular data, i.e. the stuff you generally store in spreadsheets and relational databases.
Popular in many areas, notably recommender systems.
(Cheng et al. 2016): jrzaurin/pytorch-widedeep: A flexible package to combine tabular data with text and images using Wide and Deep models in Pytorch. pytorch-widedeep, deep learning for tabular data IV: Deep Learning vs LightGBM
Note that the author of that package advises using gradient boosting machines to get this job done.
Recently, in-context inference via PFNs has had much-hyped success for tabular data.