论文标题
监督机器学习技术：带有银行业务应用的概述
Supervised Machine Learning Techniques: An Overview with Applications to Banking
论文作者
论文摘要
本文概述了监督机器学习（SML），重点是银行业的应用。涵盖的SML技术包括包装（随机森林或RF），增强（梯度提升机或GBM）和神经网络（NNS）。我们首先介绍ML任务和技术。接下来是：i）基于树的集合算法，包括带有RF的包装和加强GBM的袋装，ii）feedforward nns，iii）对超参数优化技术的讨论以及iv）机器学习解释性。本文以不同ML算法的特征进行比较。在本文中使用了银行中信用风险建模的示例，以说明技术并解释算法的结果。
This article provides an overview of Supervised Machine Learning (SML) with a focus on applications to banking. The SML techniques covered include Bagging (Random Forest or RF), Boosting (Gradient Boosting Machine or GBM) and Neural Networks (NNs). We begin with an introduction to ML tasks and techniques. This is followed by a description of: i) tree-based ensemble algorithms including Bagging with RF and Boosting with GBMs, ii) Feedforward NNs, iii) a discussion of hyper-parameter optimization techniques, and iv) machine learning interpretability. The paper concludes with a comparison of the features of different ML algorithms. Examples taken from credit risk modeling in banking are used throughout the paper to illustrate the techniques and interpret the results of the algorithms.
