You’re working with petabytes of data and need to make this dataset more manageable.

The process of reducing a dataset’s feature set while retaining its most informative components is formally known as dimensionality reduction. CPMAI describes techniques such as Principal Component Analysis (PCA) and t‐distributed Stochastic Neighbor Embedding (t-SNE) under this category, enabling teams to simplify high-dimensional data for more efficient modeling.