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Related Questions
- How do dimensionality reduction techniques like PCA and t-SNE affect the interpretability of complex relationships in high-dimensional data?
- What are some strategies for visualizing high-dimensional data to better understand non-linear relationships and their impact on model interpretability?
- Can you explain the concept of 'manifold learning' and how it can be used to uncover hidden patterns in high-dimensional data?
- What are some limitations of using linear methods for interpretability in high-dimensional spaces, and how can non-linear methods address these limitations?
- How do techniques like autoencoders and generative adversarial networks (GANs) help to uncover complex relationships in high-dimensional data?
- What role can partial dependence plots and SHAP values play in understanding non-linear relationships and their impact on model interpretability?
- Can you discuss the challenges of interpreting machine learning models in high-dimensional spaces and how to address them with techniques like feature attribution and model-agnostic interpretability methods?
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