A model explanation system

@article{Turner2016AME,
  title={A model explanation system},
  author={Ryan Turner},
  journal={2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP)},
  year={2016},
  pages={1-6}
}
We propose a new methodology for explaining the predictions of black box classifiers. We use the motivating paradigm that predictive performance is of primary importance but human analysts (e.g., in fraud detection) desire a classifier's predictions to be augmented with useful explanations. To be truly general and principled, we derive a scoring system for finding explanations based on formal requirements. In this system, the explanations are assumed to take the form of simple logical… CONTINUE READING