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Related Questions
- What are the common pitfalls of relying solely on feature importance scores to interpret machine learning models?
- How do feature importance scores ignore the interactions between variables and non-linear relationships in complex datasets?
- Can you explain the difference between feature importance scores and partial dependence plots, and when to use each?
- How do regularization techniques, such as L1 and L2 regularization, impact the calculation of feature importance scores?
- What are some alternative methods to feature importance scores for model interpretability, such as SHAP values and LIME?
- Can you discuss the relationship between feature importance scores and model performance, and how they can be misleading?
- How can feature importance scores be used to identify and mitigate bias in machine learning models?
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