Using machine learning (ML) techniques to develop disruption predictors is an effective way to avoid or mitigate the disruption in a large-scale tokamak. The recent ML-based disruption predictors have made great progress regarding accuracy, but most of them have not achieved acceptable cross-machine performance. Before we develop a cross-machine predictor, it is very important to investigate the method of developing a cross-tokamak ML-based disruption prediction model. To ascertain the elements which impact the model’s performance and achieve a deep understanding of the predictor, multiple models are trained using data from two different tokamaks, J-TEXT and HL-2A, based on an implementation of the gradient-boosted decision trees algorithm called LightGBM, which can provide detailed information about the model and input features. The predictor models are not only built and tested for performance, but also analyzed from a feature importance perspective as well as for model performance variation. The relative feature importance ranking of two tokamaks is caused by differences in disruption types between different tokamaks. The result of two models with seven inputs showed that common diagnostics is very important in building a cross-machine predictor. This provided a strategy for selecting diagnostics and shots data for developing cross-machine predictors.

使用机器学习 (ML) 技术开发中断预测器是避免或减轻大规模托卡马克中断的有效方法。最近基于 ML 的中断预测器在准确性方面取得了很大进展，但其中大多数都没有达到可接受的跨机器性能。在我们开发跨机器预测器之前，研究开发基于跨托卡马克 ML 的中断预测模型的方法非常重要。为了确定影响模型性能的元素并深入了解预测器，基于梯度提升决策树算法的实现，使用来自两种不同托卡马克（J-TEXT 和 HL-2A）的数据训练多个模型LightGBM，可以提供有关模型和输入特征的详细信息。预测器模型不仅针对性能进行构建和测试，而且还从特征重要性的角度以及模型性能变化进行分析。两个托卡马克的相对特征重要性排名是由不同托卡马克之间中断类型的差异引起的。具有七个输入的两个模型的结果表明，通用诊断对于构建跨机器预测器非常重要。这为选择诊断和镜头数据以开发跨机器预测器提供了一种策略。具有七个输入的两个模型的结果表明，通用诊断对于构建跨机器预测器非常重要。这为选择诊断和镜头数据以开发跨机器预测器提供了一种策略。具有七个输入的两个模型的结果表明，通用诊断对于构建跨机器预测器非常重要。这为选择诊断和镜头数据以开发跨机器预测器提供了一种策略。