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Related Questions
- What are the key differences between feature selection and PCA in dimensionality reduction?
- How can feature selection techniques, such as recursive feature elimination, be used to reduce the impact of increasing hyperparameters?
- What are the trade-offs between PCA and t-SNE in terms of computational time and interpretability?
- Can you provide an example of how to use PCA to reduce the dimensionality of a dataset with a large number of features?
- How does the choice of feature selection technique affect the performance of a machine learning model?
- What are some common pitfalls to avoid when using PCA for dimensionality reduction?
- Can you explain how to use a technique like LLE (Local Linear Embedding) for dimensionality reduction and how it compares to PCA?
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