Unverified50% confidenceBenchmarkExact time
On synthetic data with 10,000 samples, a 1% positive rate, and 495 negatives scoring 0.90 while the positives score 0.80 and 0.60 respectively, computing with a 0.5 threshold gives: ROC-AUC=0.9500, Average Precision=0.1449, precision=0.1681, recall=1.0000, accuracy=0.9505, TP=100, FP=495, TN=9405, FN=0.
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Long-term
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10/19/2026
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UnverifiedThe 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).79% similarUnverifiedAureaCam's composition scoring range is from 0 to 100.74% similarUnverifiedIn 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.74% similarUnverifiedUsing the behavioral features of each state for failure prediction, the AUROC on the held-out set reaches as high as 0.94.73% similarUnverifiedCalibration means that when the model outputs 0.9, across a large number of similar samples about 90% are indeed the correct class; an uncalibrated model can have high AUC but its values cannot be used directly as probabilities.73% similar
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