What are PCA and t-SNE in machine learning?

What are PCA and t-SNE in machine learning?

PCA (Principal Component Analysis) is a dimensionality reduction technique that transforms data into a lower-dimensional space. t-SNE (t-distributed Stochastic Neighbor Embedding) is another technique used for visualizing high-dimensional data by reducing it to two or three dimensions.

How do PCA and t-SNE differ in their applications?

PCA is often used for feature extraction and noise reduction, while t-SNE is primarily used for visualizing complex data structures, especially in clustering.

Can you give an example of when to use t-SNE over PCA?

Use t-SNE when you want to visualize high-dimensional data, like images or gene expression data, where preserving local structure is crucial.

What libraries in Python can I use for PCA and t-SNE?

You can use libraries like scikit-learn for both PCA and t-SNE, as well as matplotlib for visualizing the results.