Name
Abstract | ||
|---|---|---|
Interpretable machine learning tackles the important problem that humans cannot understand the behaviors of complex machine learning models and how these models arrive at a particular decision. Although many approaches have been proposed, a comprehensive understanding of the achievements and challenges is still lacking. We provide a survey covering existing techniques to increase the interpretability of machine learning models. We also discuss crucial issues that the community should consider in future work such as designing user-friendly explanations and developing comprehensive evaluation metrics to further push forward the area of interpretable machine learning. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3359786 | Communications of the ACM |
DocType | Volume | Issue |
Journal | 63 | 1 |
ISSN | Citations | PageRank |
0001-0782 | 40 | 1.40 |
References | Authors | |
34 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mengnan Du | 1 | 94 | 13.54 |
| Ninghao Liu | 2 | 121 | 12.88 |
| Xia Hu | 3 | 2411 | 110.07 |