论文标题
可解释的机器学习方法来预测慢性无家可归者
Interpretable Machine Learning Approaches to Prediction of Chronic Homelessness
论文作者
论文摘要
我们介绍了一种机器学习方法，以预测从常用的加拿大无家可归者管理信息系统中得出的识别的客户庇护所记录中的长期无家可归。使用30天的时间步骤，生成了6521个人的数据集。我们的模型HIFIS-RNN-MLP纳入了客户历史的静态和动态特征，以预测客户未来6个月的慢性无家可归者。对训练方法进行了微调，以达到高F1分数，在高召回率和精确度之间达到了预期的平衡。跨10倍交叉验证的平均召回和精度分别为0.921和0.651。采用了一种可解释性方法来解释个人预测，并洞悉导致研究人群中慢性无家可归的总体因素。该模型可以通过可解释的AI实现最新的绩效，并改善了通常是“黑匣子”神经网络模型的利益相关者信任。
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.
