InterpretML is an open-source package that incorporates state-of-the-art machine learning interpretability techniques under one roof. With this package, you can train interpretable glassbox models and explain blackbox systems. InterpretML helps you understand your model's global behavior, or understand...
2.1 Text-to-Text Framework In addition to producing state-of-the-art results on explainability datasets, this approach also allows for both "semi-supervised" training (where explanations are only provided on a subset of the dataset) and for A text-to-text model follows the sequence-to-...
@inproceedings{zhang2019axiomatic, title={Axiomatic Interpretability for Multiclass Additive Models}, author={Zhang, Xuezhou and Tan, Sarah and Koch, Paul and Lou, Yin and Chajewska, Urszula and Caruana, Rich}, booktitle={Proceedings of the 25th ACM SIGKDD International Conference on Knowledge ...
In the beginning machines learned in darkness, and data scientists struggled in the void to explain them. Let there be light. InterpretML is an open-source package that incorporates state-of-the-art machine learning interpretability techniques under one roof. With this package, you can train inter...