Visualizing Data
Statistical Models in S
Model Switching for Bayesian Classification Trees with Soft Splits
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Extracting comprehensible models from trained neural networks
Extracting comprehensible models from trained neural networks
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Complex classification models like neural networks usually have lower errors than simple models. They often have very many interdependent parameters, whose effects no longer can be understood by the user. For many applications, especially in the financial industry, it is vital to understand the reasons why a classification model arrives at a specific decision. We propose to use the full model for the classification and explain its predictive distribution by an explanation model capturing its main functionality. For a real world credit scoring application we investigate a spectrum of explanation models of different type and complexity.