ABSTRACT

The success of machine-learning methods is spreading their use to many different fields. This paper analyses one of these methods, the Kernel Logistic Regression (KLR), from the point of view of Random Utility Model (RUM) and proposes the use of the KLR to specify the utilities in RUM, freeing the modeler from the need to postulate a functional relation between the features. A Monte Carlo simulation study is conducted to empirically compare KLR with the Multinomial Logit (MNL) method, the Support Vector Machine (SVM) and the Random Forests (RF). We have shown that, using simulated data, KLR is the only method that achieves maximum accuracy and leads to an unbiased willingness-to-pay estimator for non-linear phenomena. In a real travel mode choice problem, RF achieved the highest predictive accuracy, followed by KLR. However, KLR allows for the calculation of indicators such as the value of time, which is of great importance in the context of transportation. 

抽象的

机器学习方法的成功正在将其应用扩展到许多不同的领域。本文从随机效用模型（RUM）的角度分析了其中一种方法，即内核逻辑回归（KLR），并提出了使用KLR来指定RUM中的效用的方法，从而使建模者无需进行假设功能之间的功能关系。进行了蒙特卡洛模拟研究，以经验方式将KLR与多项式Lo​​git（MNL）方法，支持向量机（SVM）和随机森林（RF）进行比较。我们已经表明，使用模拟数据，KLR是唯一可实现最大准确性并导致针对非线性现象进行公正的支付意愿估计量的方法。在实际的出行模式选择问题中，RF达到了最高的预测准确度，其次是KLR。然而，