Insights from Inside Neural Networks
64 Pages Posted: 19 Aug 2018 Last revised: 24 Apr 2020
Date Written: April 23, 2020
We provide a tutorial that illuminates the aspects which need to be considered when fitting neural network regression models to claims frequency data in insurance. We discuss feature pre-processing, choice of loss function, choice of neural network architecture, class imbalance problem, balance property and bias regularization as well as over-fitting. This discussion is based on a publicly available real car insurance data set.
Keywords: Neural Networks, Architecture, Over-Fitting, Loss Function, Dropout, Regularization, LASSO, Ridge, Gradient Descent, Class Imbalance, Car Insurance, Claims Frequency, Poisson Regression Model, Machine Learning, Deep Learning
JEL Classification: G22, C10, C13, C14, C67
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