Glossary · Phase 3 · Machine learning
Dimensionality reduction
Squeezing many columns into a few while keeping most of the information, to speed models up or to draw data in 2D.
Try it now
Turn the line and see how much of the spread survives. PCA simply finds the best direction, then the next best, and so on.
Learn it properly
Dimensionality reduction · Visualize and compress high-dimensional data. About 30 minutes.
Other terms from the same lesson
- explained variance ratio: The share of the data's spread that each PCA component keeps. Adding them up shows how much a few components hold.
- t-SNE: A way to draw high-dimensional data in 2D that keeps near neighbors together. Good for pictures, not as model input.
- UMAP: A faster cousin of t-SNE for drawing high-dimensional data, such as embeddings, in 2D.
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