Maximilian
Maximilian
First, I want to thank you very much for providing this toolkit! I am eager to use your implementation for my own research! Unfortunately, as I was working through the...
Next to interventional approaches also observational explanations should be created with shapiq. For this conditional modelling and sampling of and from the tabular data distributions is required. This [arfpy package](https://arxiv.org/abs/2311.07366)...
Add the structured sampling SV estimator. ## Tasks - [ ] Add the approximator - [ ] Add tests - [ ] Add the documentation and the correct reference to...
Add validator to convert CatBoost Trees to internal model.
## Description The `InteractionValues` dataclass is the core data object containing the results and of approximators and explainers. For representing Shapley values (SVs) visually, the _shap_ package already contains a...
Currently, not the whole API is visible in the docs. Not all modules can be found. We need to add this before the initial release. For example there are no...
We need to be able to naturally support computing interactions for XGBoost Type of models. This needs to be done in the conversion mechanism where tree-models are transformed into their...
We need to support LightGBM naturally with TreeSHAP-IQ. For this we need to have a conversion mechanism. This happens in the conversion into the edge representation of the tree models....
Add the correct paper references as links to the docstrings of the approximators and explainers (in case of treeshapiq).
Write an example notebook showcasing how to use TreeSHAP-IQ for SOTA tree models. An illustrative example is the California housing dataset which shows easy to understand interactions in the latitude...