Interpretable generalized additive neural networks
提出一种结合梯度提升和定制神经网络的机器学习模型IGANN，在保持高预测性能的同时实现可解释性，适用于信用评分、犯罪再犯预测等场景。
We propose Interpretable Generalized Additive Neural Networks (IGANN), a novel machine learning model that uses gradient boosting and tailored neural networks to obtain high predictive performance while being interpretable to humans. We derive an efficient training algorithm based on the theory of extreme learning machines, that allows reducing the training process to solving a sequence of regularized linear regressions. We analyze the algorithm theoretically, provide insights into the rate of change of so-called shape functions, and show that the computational complexity of the training process scales linearly with the number of samples in the training dataset. We implement IGANN in PyTorch, which allows the model to be trained on graphics processing units (GPUs) to speed up training. We demonstrate favorable results in a variety of numerical experiments and showcase IGANN’s value in three real-world case studies for productivity prediction, credit scoring, and criminal recidivism prediction.