What are some common dimensionality reduction techniques?
An example of an AI chatbot conversation about dimensionality reduction techniques in data science.
What are some common dimensionality reduction techniques?
Some common techniques include PCA, t-SNE, and UMAP. Would you like to know more about any specific one?
Can you explain PCA?
Principal Component Analysis (PCA) reduces dimensionality by transforming data to a new set of variables, the principal components.
What is t-SNE used for?
t-SNE is often used for visualizing high-dimensional data in a lower-dimensional space, preserving the structure.
How does UMAP differ from t-SNE?
UMAP is generally faster and can preserve more of the global structure compared to t-SNE.
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