Combine and conquer: model averaging for out-of-distribution forecasting
组合取胜：用于分布外预测的模型平均AI 解读 ↓
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组合取胜：用于分布外预测的模型平均
- 研究问题
- 出行距离超出训练样本范围时，不同模型还能否准确预测交通方式选择？作者研究能否根据预测对象偏离训练数据的程度组合模型。
- 方法与思路
- 作者组合计量、心理学与机器学习模型，使权重随预测出行特征与估计样本特征之间的距离变化，并在两个案例中检验表现。
- 材料陈述的贡献
- 摘要称，距离越超出训练范围，组合越偏重行为或计量基础较强的模型；两个案例中，组合在样本内、测试集及范围外预测均有改善。
- 阅读关注点
- 应核对特征距离和权重函数的定义、权重估计是否使用测试信息，并检查距离外推划分、比较模型调参及选择概率评价指标。
- 阅读边界
- 这是 arXiv 预印本，不能据此确认同行评审或期刊接收。性能改善限于摘要所述两个案例，未提供数值或对其他分布变化的理论保证。 复核沿用经核验的旧摘要解读，未重新调用模型或阅读新版全文。
原文摘要
Travel behaviour modellers have an increasingly diverse set of models at their disposal, ranging from traditional econometric structures to models from mathematical psychology and data-driven approaches from machine learning. A key question arises as to how well these different models perform in forecasting, especially when considering trips of different characteristics from those used in estimation, i.e. out-of-distribution prediction, and whether better predictions can be obtained by combining insights from the different models. We focus on trip distance as a key example of a variable where the application context might go beyond the estimation data. Across two case studies, we show that while data-driven approaches excel in predicting mode choice for trips within the distance bands used in estimation, beyond that range, the picture is fuzzy. To leverage the relative advantages of the different model families and capitalise on the notion that multiple `weak' models can result in more robust models, we put forward the use of a model averaging approach that allocates weights to different model families as a function of the distance between the characteristics of the trip for which predictions are made, and those used in model estimation. Overall, we see that the model averaging approach gives larger weight to models with stronger behavioural or econometric underpinnings the more we move outside the interval of trip distances covered in estimation. Across both case studies, we show that our model averaging approach obtains improved performance both on the estimation and test data, and crucially also when predicting mode choices for trips of distances outside the range used in estimation.