7
Conclusion
Explaining AI through visual explanations and efficient interpretable models
Front matter
1
Introduction
2
Visual Explanations of the Explainers, to Gain Insight into Predictions from High-Dimensional Models
3
Random Seeds, Model Stability, and the Complexity-Parsimony Trade-off
4
kultarr: Revisiting Anchors with a different lens for visualisation
5
kumquat: A simpler lime
6
sillysplines: Data generation using linear splines
7
Conclusion
Bibliography
7
Conclusion
6
sillysplines: Data generation using linear splines
Bibliography