A machine learning algorithm was applied to predict the onset of flashover in archival experiments in a 1/5 scale ISO 13784-1 enclosure constructed with sandwich panels. The experiments were performed to assess whether a small-scale model could provide a better full-scale correlation than the single burning item test. To predict the binary output, a regularized logistic regression model was chosen as ML environment, for which lasso-regression significantly reduced the amount of variance at a negligible increase in bias. With the regularized model, it was possible to discern the predictive variables and determine the decision boundary. In addition, a methodology was put forward on how to use the to update the learning algorithm iteratively. As a result, it was shown how a learning algorithm can be used to facilitate ongoing experimentation. At first as a crude guideline, and in later stages, as an accurate prediction algorithm. It is foreseen that, by iteratively updating the algorithm, by compiling existing and new experiments in databases, and by applying fire safety knowledge, the final learned algorithm will be able to make accurate predictions for unseen samples and test conditions.

中文翻译：

---

知识测试：应用机器学习技术预测 1/5 比例 ISO 13784-1 外壳中的闪络

应用机器学习算法来预测在由夹心板构成的 1/5 比例 ISO 13784-1 外壳中的档案实验中发生闪络。进行实验是为了评估小规模模型是否可以提供比单个燃烧项目测试更好的全面相关性。为了预测二元输出，选择了正则化逻辑回归模型作为 ML 环境，对于该模型，套索回归在偏差增加可忽略不计的情况下显着减少了方差量。使用正则化模型，可以识别预测变量并确定决策边界。此外，还提出了如何使用 迭代更新学习算法的方法。结果，展示了如何使用学习算法来促进正在进行的实验。起初作为粗略的指导方针，在以后的阶段，作为准确的预测算法。可以预见，通过迭代更新算法，通过在数据库中编译现有的和新的实验，并通过应用消防安全知识，最终学习到的算法将能够对看不见的样本和测试条件做出准确的预测。