Data Science
Feature Engineering for Tabular ML in 2026: What Still Beats a Transformer
Well-engineered features plus gradient boosting still beat transformers on most tabular ML problems in 2026 — at lower cost, latency, and risk. Here's how.
Topic
#XGBoost
Everything tagged “XGBoost” across News, Learn, Research and Interviews.
Machine Learning
Classical ML in 2026: Why Tabular Models Still Win Production Budgets
Why classical ML and gradient boosting still win production budgets for tabular data in 2026 — accuracy, cost, and a model selection framework.