可解释的机器学习方法预测长期无家可归
Interpretable Machine Learning Approaches to Prediction of Chronic Homelessness
摘要：
我们引入了一种机器学习方法来预测长期无家可归的去识别客户端住房记录从一个常用的加拿大无家可归者管理信息系统。使用30天的时间步长，生成了6521个个体的数据集。我们的模型，HIFIS-RNN-MLP，结合静态和动态功能的客户端的历史来预测慢性无家可归6个月到客户端的未来。对训练方法进行了微调，以获得较高的F1分数，从而在高召回率和准确率之间实现所需的平衡。10倍交叉验证的平均召回率和精确率分别为0.921和0.651。解释性方法被用来解释个人的预测，并深入了解导致研究人群中长期无家可归的整体因素。该模型通过可解释的AI实现了最先进的性能，并提高了利益相关者对通常是“黑匣子”神经网络模型的信任。
英文摘要：
We introduce a machine learning approach to predict chronic homelessness from de-identified client shelter records drawn from a commonly used Canadian homelessness management information system. Using a 30-day time step, a dataset for 6521 individuals was generated. Our model, HIFIS-RNN-MLP, incorporates both static and dynamic features of a client's history to forecast chronic homelessness 6 months into the client's future. The training method was fine-tuned to achieve a high F1-score, giving a desired balance between high recall and precision. Mean recall and precision across 10-fold cross validation were 0.921 and 0.651 respectively. An interpretability method was applied to explain individual predictions and gain insight into the overall factors contributing to chronic homelessness among the population studied. The model achieves state-of-the-art performance and improved stakeholder trust of what is usually a "black box" neural network model through interpretable AI.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
