All glossary terms
techniquesintermediate

Dimensionality Reduction

Techniques for reducing the number of features while preserving important information.

In more detail

Methods include PCA (linear), t-SNE (visualization), UMAP (non-linear), and autoencoders. Reduces overfitting, improves efficiency, and enables visualization. Tradeoff: may lose information.

Examples

  • PCA for image compression
  • t-SNE for visualization
  • Autoencoder for feature extraction
  • Feature selection

Related terms

Feature SelectionFeature Engineering
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