Unverified50% confidenceBenchmarkExact time
In 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.
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Long-term
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10/12/2026
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UnverifiedIn that heart disease prediction experiment, the logistic regression model achieved an AUC of 0.89.64% similarUnverifiedIn a benchmark of 30 held-out test windows, TabPFN achieved 100.0% overall accuracy, 100.0% shock recall, 0.33 ms CPU latency, 0.0% step-frequency false alarms, and required no training or tuning.63% similarUnverifiedIn the benchmark test, Logistic Regression achieved 86.7% overall accuracy, 100.0% impact recall, and 0.0% cadence false positives.63% similarUnverifiedFor the 7B model, accuracy with the solver was 75.31% versus 72.02% for direct calculation, a paired improvement of +3.29 percentage points, with a 95% cluster interval of [-3.49, 10.38] crossing zero, making the advantage non-significant.63% similarUnverifiedThe accuracy data in the test has an error margin of approximately ±8–10 percentage points at n=100 sample size (based on a 95% confidence-level binomial confidence interval).63% similar
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