TabPFN hits 98.8% accuracy in 0.47 s, beating Random Forest and CatBoost
Why does a model that skips traditional training matter? While most tabular learners spend minutes—or even hours—building trees, TabPFN leans on in‑context learning, essentially treating the dataset as a prompt.