Quantitative Analysis of Explainable AI (XAI) Visualizations
Our study proposes a quantitative approach for evaluating model explanations in XAI, enhancing transparency and trust in AI systems.
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Cite As
Simhadri, C. G., & Kondaveeti, H. K. (2024), Quantitative Analysis of Explainable AI (XAI) Visualizations (https://www.mathworks.com/matlabcentral/fileexchange/<...>), MATLAB Central File Exchange. Retrieved September 26, 2024.
General Information
- Version 1.0.2 (7.84 KB)
MATLAB Release Compatibility
- Compatible with R2021a to R2024b
Platform Compatibility
- Windows
- macOS
- Linux
| Version | Published | Release Notes | Action |
|---|---|---|---|
| 1.0.2 | Updated the quantitative_evaluation_metrics and overfitting_ratio files |
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| 1.0.1 | 1. Train and save the pre-trained model: Train the model using specified settings, evaluate its performance using various performance evaluation metrics, and save the trained model for future use (Train_pre-trained_model.m). |
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| 1.0.0 |
