We lifted 4 functions out of this paper's own repositories and ran 4 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| hbaniecki/robust-feature-effects | canonical | 4 of 4 |
| Function | Status | Where it lives |
|---|---|---|
| check_early_stopping | Ran | hbaniecki/robust-feature-effects/src/utils.py code served (permissive licence) · get_code("6d5caa9f8bb4b5f7") |
| distance_values | Ran | hbaniecki/robust-feature-effects/src/loss.py code served (permissive licence) · get_code("3b200194bc918ad6") |
| loss | Ran | hbaniecki/robust-feature-effects/src/loss.py code served (permissive licence) · get_code("831e0df4b6f765d1") |
| loss_pop | Ran | hbaniecki/robust-feature-effects/src/loss.py code served (permissive licence) · get_code("70b16a7fc8254617") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We study the robustness of global post-hoc explanations for predictive models trained on tabular data. Effects of predictor features in black-box supervised learning are an essential diagnostic tool for model debugging and scientific discovery in applied sciences. However, how vulnerable they are to data and model perturbations remains an open research question. We introduce several theoretical bounds for evaluating the robustness of partial dependence plots and accumulated local effects. Our experimental results with synthetic and real-world datasets quantify the gap between the best and worst-case scenarios of (mis)interpreting machine learning predictions globally.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2406.09069")
get_code_for_paper("2406.09069")
have("2406.09069")
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