Unverified50% confidenceBenchmarkExact time
In offline MiniBooNE experiments, JevForest achieved an accuracy of 0.845±0.010 using ten features, improving to 0.885±0.008 using forty features.
1
Sources
50%
Confidence
Long-term
Relevance
10/9/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedThe hosted model Jev is significantly more accurate on 9 out of 11 decision points, with margins ranging from +10.8 to +46.0 percentage points.73% similarUnverifiedIn the fourth round of fusion experiments, treating JEV as the 9th framework and taking the highest score yielded an overall accuracy of 81.5%, but parity dropped back to 50%.71% similarUnverifiedIn the configuration using ten policy snapshots, using only 8 prompts achieved an average accuracy of 24.09%, approaching the 24.51% achieved with 14,080 prompts.69% 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.68% similarUnverifiedLID accuracy at switching points is typically 10–20 percentage points lower than LID accuracy at non-switching points.68% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/990779API
curl https://kongchang.com/api/v1/knowledge/claims/990779MCP
get_claim(id=990779)