👉 Bean Math is a set of mathematical operations and transformations that can be applied to data points in a dataset, primarily used for dimensionality reduction and feature extraction. It's built around the idea of projecting high-dimensional data into a lower-dimensional space while preserving as much variance (or "information") as possible. The core operations include projection (mapping data onto a lower-dimensional subspace), rotation (orienting the subspace to align with the principal directions of variance), and scaling (adjusting the magnitude of the data along these new axes). Bean Math leverages singular value decomposition (SVD) to identify and retain the most significant features, effectively reducing noise and redundancy. This process is crucial in machine learning for simplifying complex datasets, enhancing model performance, and visualizing high-dimensional data more easily.
