Unverified50% confidenceBenchmarkExact time
In the prediction experiments the author ran, TabPFN ranked first in all 9 runs: ROC-AUC was 0.721 with 300 historical bookings, 0.766 with 1,000, and 0.778 with 3,000; gradient boosting scored 0.682, 0.702 and 0.747 respectively; logistic regression scored 0.699, 0.741 and 0.749 respectively.
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10/18/2026
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UnverifiedUnder the condition of a single table and zero feature engineering, TabFM outperformed the author's previously carefully tuned tree model in a customer churn prediction demo, but the score was only 0.60.69% similarUnverifiedTabPFN-2's median prediction had an average error as a share of sales of 0.502, versus 0.486 for the 4-week mean × k baseline; TabPFN-2 also outperformed a gradient boosting model trained on the same features (0.527), the last-week rule (0.631), and the same-week-last-year rule (0.884).69% similarUnverifiedIn this backtest, ordinary logistic regression and linear regression had accuracy comparable to TabPFN (AUC 0.890, Brier 0.122, payment-day error 5.81 days) and were not noticeably inferior.68% similarUnverifiedIn a fair comparison using only the baseline window input, PRISM's AUROC was 0.8895, broadly on par with UniPACT's 0.8937 and MDS-ED's 0.8889.68% similarUnverifiedA learner, when detecting forged receipts on this dataset, found that regardless of how the model was adjusted, the ROC-AUC consistently hovered between 0.46 and 0.54, close to random guessing.67% similar
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