What are some common dimensionality reduction techniques in Python?
An example of an AI chatbot conversation about Python machine learning dim reduction techniques.
What are some common dimensionality reduction techniques in Python?
Some common techniques include PCA, t-SNE, and LDA. Would you like to know more about any specific one?
Can you explain PCA?
PCA, or Principal Component Analysis, reduces dimensionality by transforming to a new set of variables that are uncorrelated.
How does t-SNE differ from PCA?
t-SNE is better for visualizing high-dimensional data in lower dimensions, focusing on preserving local structures.
When should I use LDA instead of PCA?
Use LDA when you want to maximize class separability, while PCA focuses on variance without considering class labels.
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