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Related Questions
- How do feature importance scores help in understanding the contribution of individual features to a model's predictions in the context of context-aware metrics?
- Can you provide examples of techniques used to calculate feature importance scores, such as permutation importance, SHAP values, and partial dependence plots?
- How does feature importance aid in identifying and addressing potential bias in context-aware models, particularly in cases where features are highly correlated with each other?
- In what ways can feature importance be used to improve the interpretability of complex models, such as those using gradient boosting or neural networks?
- How does feature importance relate to model explainability, and what are some best practices for using feature importance in conjunction with other interpretability techniques?
- Can you discuss the challenges and limitations of calculating feature importance in high-dimensional datasets, and how to address them?
- How can feature importance be used to identify the most influential features in a model, and what are the implications of feature importance for feature selection and dimensionality reduction?
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