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Related Questions
- What are the benefits of using PCA for dimensionality reduction in model-agnostic interpretability methods?
- How does t-SNE compare to PCA in terms of preserving the structural information of the data?
- What are the limitations of using linear dimensionality reduction techniques like PCA for high-dimensional feature spaces?
- Can you explain the concept of manifold learning and its application to dimensionality reduction?
- What is the role of feature selection in reducing dimensionality and improving model interpretability?
- How does feature extraction through techniques like Independent Component Analysis (ICA) improve model interpretability?
- What are some common pitfalls to avoid when applying dimensionality reduction techniques to high-dimensional feature spaces?
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