Capítulo 16 Dimensionality reduction

Learning objectives:

• Create, prep, and bake recipes outside of a workflow to test or debug the recipes.
• Compare and contrast dimensionality reduction techniques (techniques used to create a small set of features that capture the main aspects of the original predictor set).
• Use principal component analysis (PCA) to reduce dimensionality.
• Use partial least squares (PLS) to reduce dimensionality.
• Use independent component analysis (ICA) to reduce dimensionality.
• Use uniform manifold approximation and projection (UMAP) to reduce dimensionality.
• Use dimensionality reduction techniques in conjunction with modeling techniques.