This document describes an approach used in the Multi-Machine Disruption
Prediction Challenge for Fusion Energy by ITU, a data science competition which
ran from September to November 2023, on the online platform Zindi. The
competition involved data from three fusion devices - C-Mod, HL-2A, and J-TEXT
- with most of the training data coming from the last two, and the test data
coming from the first one. Each device has multiple diagnostics and signals,
and it turns out that a critical issue in this competition was to identify
which signals, and especially which features from those signals, were most
relevant to achieve accurate predictions. The approach described here is based
on extracting features from signals, and then applying logistic regression on
top of those features. Each signal is treated as a separate predictor and, in
the end, a combination of such predictors achieved the first place on the
leaderboard.

该研究论文描述了在 2023 年 9 月至 11 月期间 ITU 举办的多机器扰动预测挑战赛中所采用的方法，该比赛在在线平台 Zindi 上进行，涉及了 C-Mod、HL-2A 和 J-TEXT 三个聚变设备的数据，其中大部分训练数据来自后两者，而测试数据来自第一个设备。这篇论文采用了从信号中提取特征并在其上应用逻辑回归的方法，将每个信号视为一个单独的预测器，最终通过这些预测器的组合在排行榜上取得了第一名的成绩。

融合装置中的干扰预测：通过特征提取与逻辑回归

Disruption Prediction in Fusion Devices through Feature Extraction and  Logistic Regression

We present three case studies of organizations using a data science
competition to answer a pressing question. The first is in education where a
nonprofit that creates smart school budgets wanted to automatically tag budget
line items. The second is in public health, where a low-cost, nonprofit women's
health care provider wanted to understand the effect of demographic and
behavioral questions on predicting which services a woman would need. The third
and final example is in government innovation: using online restaurant reviews
from Yelp, competitors built models to forecast which restaurants were most
likely to have hygiene violations when visited by health inspectors. Finally,
we reflect on the unique benefits of the open, public competition model.

本文介绍了三个案例，分别为教育、公共卫生和政府创新领域，这些组织采用数据科学竞赛来回答紧迫问题，并反思了公开竞赛模式的独特优势。