| Title |
Influence functions - comparison of existing methods / |
| Translation of Title |
Įtakos funkcijos - egzistuojančių metodų palyginimas. |
| Authors |
Balkytė, Austėja |
| Full Text |
|
| Pages |
29 |
| Keywords [eng] |
Influence functions, RelatIF, explainability |
| Abstract [eng] |
In order to solve the problem of explainability of machine learning (ML) models, many solutions have been suggested in the recent years. One of them is a technique from robust statistics - influence functions (IF). However, many papers indicate that IF return outliers and mislabelled data as the most influential points from training set. In order to identify relevant examples that are more understandable for end users, alternative method was suggested: relative influence functions (RelatIF). In this paper, we compare how this method works on models where influence functions fail. |
| Dissertation Institution |
Vilniaus universitetas. |
| Type |
Master thesis |
| Language |
English |
| Publication date |
2022 |
