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Related Questions
- What are some common criticisms of the SHAP value method for model interpretability, and how do they affect the understanding of model decisions?
- Can SHAP values be misleading when dealing with complex interactions between features, and if so, how?
- How do SHAP values handle non-linear relationships between features and the target variable, and what are the implications for model interpretability?
- Can SHAP values be used to interpret models with high-dimensional feature spaces, and if so, what are the challenges involved?
- How do SHAP values compare to other model interpretability methods, such as LIME or partial dependence plots?
- What are some scenarios where SHAP values may not accurately capture the underlying relationships between features and the target variable?
- Can SHAP values be used to explain model decisions in the presence of missing or noisy data, and if so, how?
- How do SHAP values account for feature correlations and multicollinearity, and what are the implications for model interpretability?
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