Dimensionality Reduction (t-SNE, UMAP Overview)

Summary

Advanced, non-linear techniques for visualizing high-dimensional data in 2D or 3D — t-SNE and UMAP preserve local structure well, making them popular for exploring clusters visually, though they're not designed for use as model input features.

• 1Visualize a high-dimensional dataset in 2D using t-SNE or UMAP and compare to PCA.
• 2Learn why t-SNE/UMAP are primarily visualization tools, not general-purpose preprocessing.
• 3Understand these techniques are non-deterministic — rerunning can produce different layouts.