Summary of Explainable Artificial Intelligence For Dependent Features: Additive Effects Of Collinearity, by Ahmed M Salih
Explainable Artificial Intelligence for Dependent Features: Additive Effects of Collinearity
by Ahmed M Salih
First submitted to arxiv on: 30 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Additive Effects of Collinearity (AEC) method addresses the limitations of current Explainable Artificial Intelligence (XAI) approaches in dealing with collinearity, a common issue in machine learning models. AEC divides multivariate models into univariate models to examine the impact of each feature on the outcome, considering the effects of collinearity. This novel XAI approach is evaluated using simulated and real data, comparing its performance with state-of-the-art methods. The results demonstrate AEC’s robustness and stability in explaining AI models, even when faced with collinearity issues. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to explain artificial intelligence (AI) models has been developed. This method helps understand how each feature in the model affects the prediction outcome. Currently, most AI explanation methods assume that features are independent, but this is not always true. The proposed Additive Effects of Collinearity (AEC) method addresses this issue by looking at how multiple features interact with each other and their impact on the outcome. AEC was tested using fake and real data to see if it’s better than existing methods. The results show that AEC is more reliable and consistent in explaining AI models, even when dealing with complex relationships between features. |
Keywords
» Artificial intelligence » Machine learning
