Building Linear Discriminant Functions Using Minimum Squared Error Criterion (MSE Criterion) Through Training Sample Sets

This paper applies the Minimum Squared Error Criterion (MSE Criterion) to construct linear discriminant functions from training datasets and utilizes these functions for test set classification. The implementation uses three feature datasets: 1) Gender data (male/female), 2) SONA academic metrics, and 3) UPS performance scores, with Python/numpy implementations for matrix operations and weight optimization.