论文标题
神经添加剂模型的普遍树林：在金融中追求透明，准确的机器学习模型
Generalized Groves of Neural Additive Models: Pursuing transparent and accurate machine learning models in finance
论文作者
论文摘要
尽管机器学习方法比传统方法显着改善了模型性能，但它们的黑盒结构使研究人员很难解释结果。对于高度监管的金融行业，模型透明度对于准确性同样重要。在不了解模型的工作原理的情况下，即使是高度准确的机器学习方法也不太可能被接受。我们通过引入一种新颖的透明机器学习模型来解决这个问题，称为神经添加剂模型的广义格罗夫。神经添加剂模型的广义树林将特征分为三类：线性特征，单个非线性特征和相互作用的非线性特征。另外，最后类别中的交互仅是本地的。逐步选择算法可区分线性和非线性组件，并且通过应用加法分离标准仔细验证了相互作用的组。通过金融中的一些经验例子，我们证明了神经添加剂模型的广义林具有高精度和透明度，主要是线性项，仅稀疏的非线性术语。
While machine learning methods have significantly improved model performance over traditional methods, their black-box structure makes it difficult for researchers to interpret results. For highly regulated financial industries, model transparency is equally important to accuracy. Without understanding how models work, even highly accurate machine learning methods are unlikely to be accepted. We address this issue by introducing a novel class of transparent machine learning models known as generalized groves of neural additive models. The generalized groves of neural additive models separate features into three categories: linear features, individual nonlinear features, and interacted nonlinear features. Additionally, interactions in the last category are only local. A stepwise selection algorithm distinguishes the linear and nonlinear components, and interacted groups are carefully verified by applying additive separation criteria. Through some empirical examples in finance, we demonstrate that generalized grove of neural additive models exhibit high accuracy and transparency with predominantly linear terms and only sparse nonlinear ones.
