Comprehensible Explanation of Predictive Models

Abstract

The most successful prediction models (e.g., SVM, neural networks, or boosting) unfortunately do not provide explanations of their predictions. In many important applications of machine learning the comprehension of the decision process is of uttermost importance and dominates the classification accuracy, e.g., in business and medicine. This chapter introduces general explanation methods that are independent of the prediction model and can be used with all classification models that output probabilities. It explains how the methods work and graphically explains models' decisions for new unlabelled cases. The approach is put in the context of applications from medicine, business and macro economy.