Explainability
Dimensionality Reduction
Reduces complexity for understanding (e.g., PCA, t-SNE, UMAP)
4 techniques in this subcategory
4 techniques
| Goals | Models | Data Types | Description | |||
|---|---|---|---|---|---|---|
| Factor Analysis | Algorithmic | Architecture/model Agnostic Paradigm/unsupervised +1 | Tabular | Factor analysis is a statistical technique that identifies latent variables (hidden factors) underlying observed... | ||
| Principal Component Analysis | Algorithmic | Architecture/model Agnostic Paradigm/unsupervised +1 | Any | Principal Component Analysis transforms high-dimensional data into a lower-dimensional representation by finding the... | ||
| t-SNE | Visualization | Architecture/model Agnostic Requirements/black Box | Any | t-SNE (t-Distributed Stochastic Neighbour Embedding) is a non-linear dimensionality reduction technique that creates 2D... | ||
| UMAP | Visualization | Architecture/model Agnostic Requirements/black Box | Any | UMAP (Uniform Manifold Approximation and Projection) is a non-linear dimensionality reduction technique that creates 2D... |
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