🧠 XAI on Tabular Models — Interpretability

This project explores explainability techniques on tabular data using linear regression, random forests and GAM. We apply XAI methods — including t-values interpretation, feature importance for random forests and SHAP as a post hoc method — to better understand model predictions and decision logic.

Due to data confidentiality constraints, the datasets used in this study are not publicly available in this repository.

Contents

1. Data analysis
2. Machine learning interpretation (linear regression, random forest and GAM)
3. SHAP post hoc method