Tabular Machine Learning
Classification and regression on tabular data. Benchmarked on TabArena (Elo over 51 datasets); gradient-boosted trees vs tabular foundation models like TabPFN.
Tabular Machine Learning is a key task in time series. Below you will find the standard benchmarks used to evaluate models, along with current state-of-the-art results.
Benchmarks & SOTA
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Tabular Classification
Tabular classification — predicting discrete labels from structured rows and columns — remains the one domain where gradient-boosted trees (XGBoost, LightGBM, CatBoost) stubbornly rival deep learning. Despite years of effort, neural approaches like TabNet (2019) and FT-Transformer (2021) only match tree methods on certain splits, and a 2022 NeurIPS study by Grinsztajn et al. confirmed that trees still dominate on medium-sized datasets. The real frontier is AutoML systems (AutoGluon, FLAML) that ensemble both paradigms, and the emerging question of whether foundation models pretrained on millions of tables can finally tip the balance.
Tabular Regression
Tabular regression — predicting continuous values from structured data — powers everything from house-price estimation to demand forecasting and shares the same tree-vs-neural tension as classification. XGBoost and LightGBM remain brutally effective defaults, but recent work on differentiable trees and table-aware transformers (TabPFN, 2022) showed that meta-learned priors can beat tuned GBDTs on small datasets in seconds. The challenge is distribution shift: real-world regression targets drift over time, and most benchmarks (UCI, Kaggle) are static snapshots that hide this problem entirely.
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