Su-In Lee

TL;DR: In this article, a unified framework for interpreting predictions, SHAP (SHapley Additive exPlanations), is presented, which assigns each feature an importance value for a particular prediction.

TL;DR: An explanation method for trees is presented that enables the computation of optimal local explanations for individual predictions, and the authors demonstrate their method on three medical datasets.

TL;DR: The aspergilli comprise a diverse group of filamentous fungi spanning over 200 million years of evolution, and a comparative study with Aspergillus fumigatus and As pergillus oryzae, used in the production of sake, miso and soy sauce, provides new insight into eukaryotic genome evolution and gene regulation.

TL;DR: In this paper, a unified framework for interpreting predictions, SHAP (SHapley Additive exPlanations), is presented, which assigns each feature an importance value for a particular prediction.

TL;DR: The results suggest that if anaesthesiologists currently anticipate 15% of hypoxaemia events, with the assistance of this system they could anticipate 30%, a large portion of which may benefit from early intervention because they are associated with modifiable factors.