Jan 1, 2024 · In this paper, we consider two popular model-agnostic interpretation methods — LIME and SHAP, and study their interpretative performance over ...
Interpretable machine learning for imbalanced credit scoring datasets · 1. Data Pre-processing · 2. Initial Sampling for Local Interpretation. Randomly sample.
Jun 3, 2023 · The results show that interpretations gener- ated from LIME and SHAP are less stable as the class imbalance increases, which indicates that the ...
Jan 1, 2024 · The results show that interpretations generated from LIME and SHAP are less stable as the class imbalance increases, which indicates that the ...
Jul 1, 2024 · The results show that interpretations generated from LIME and SHAP are less stable as the class imbalance increases, which indicates that the ...
Dec 15, 2022 · Existing deep learning methods trained with imbalanced data typically use a single metric to describe model performance on all classes. We ...
Jul 21, 2023 · Our paper provides an experimental framework including a controlled sampling process and two novel stability indexes to analyse how the ...
Molnar, C. (2021). Interpretable machine learning: A guide for making black box models explainable. https://christophm.github.io/interpretable-ml-book/cite.html ...
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The DTE can compensate for the trade-off between the two requirements of a credit scoring model. It addresses imbalanced data by combining the over-sampling and ...
The AutoScore-. Imbalance tool has the potential to be applied to highly unbalanced datasets to gain further insight into rare medical events and to facilitate ...