dimensionality-reduction

Reduce many features to fewer, and choose between PCA, Fisher linear discriminant analysis, feature selection and 2-D visualisation methods. Use when a dataset has dozens or hundreds of correlated columns; when someone asks whether to use PCA, how many principal components to keep, or what the loadings mean; how to visualise high-dimensional data or embeddings; whether to use t-SNE or UMAP; how to remove noise or multicollinearity; when to use LDA to separate classes; or which features to keep or drop. Not for grouping records into segments, and not for creating new features from raw data.