XGBoost

Headlines · 3

1. 1d
   Developer reports XGBoost forecasts county power outages two hours ahead

   A developer built an hourly XGBoost system for 216 counties in Missouri and Illinois that estimates the probability of at least 5% of a county’s customers newly losing power during a six-hour window beginning two hours ahead. The model uses issued HRRR storm forecasts, county-specific information, current outages and neighboring-county data. It was trained on 2020–2024 and tested on 2025. The developer reported a PR-AUC of 0.094, compared with 0.039 for a rate-table baseline using National Weather Service warnings and Storm Prediction Center Day 1 outlooks. At equal alert volume, the developer reported catching 30% of new outages versus 17%, with 15% precision versus 11%. The base rate was 0.5% of county-hours. An early transfer test reportedly caught 31% versus 19% in Iowa, Indiana and Michigan.

   r/MachineLearning · 🔥 2
2. Nov 2
   Epi2Diff predicts human item difficulty from LLM reasoning traces

   Researchers from Virginia Tech, the University of Maryland, MBZUAI and the University of Pittsburgh proposed Epi2Diff, a framework that splits LLM reasoning traces into cognitive episodes and combines them with item content to predict human difficulty. The research team reported first-place results across the main metrics for SAT Mathematics, SAT Reading and Writing, Cambridge and USMLE, with SAT Mathematics accuracy reaching 73.0%. Its paper was accepted by Transactions on Machine Learning Research.

   36氪 人工智能 · 🔥 0
3. Oct 20
   VX-One paper proposes typed, calibrated decision heads for network perimeter triage

   A single-author preprint, VX-One, was submitted to arXiv on 21 October 2026 by Juan S. Santillana, describing a small multi-head network of 100K–10M parameters whose outputs are a calibrated gate, a choice over a closed candidate set that always includes an explicit unknown option, and an ordinal score, at roughly 0.7–1.7 ms median latency on CPU. In the author's own experiments on UNSW-NB15, CIC-IDS2017 and EMBER2024 against calibrated XGBoost, Random-Forest, MLP and Kitsune, VX-One reportedly wins detection (AUROC/FPR@95%TPR) on UNSW-NB15 and under temporal drift on CIC-IDS2017 (0.711 vs 0.637), while XGBoost wins multi-class attribution on all three benchmarks. The paper also reports that calibration collapses under temporal drift (ECE 0.04 to 0.40) for every method, that abstention thresholds transfer poorly across benchmarks, and that on payload-less reconstructions signature engines resolve only about 0.1% of flows, leaving a gray zone the second cascade phase recovers to combined recall of at least 0.978.

   Hugging Face · Papers · 🔥 0

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