Fisher's linear discriminant

Fisher's linear discriminant is a statistical method used to distinguish between two groups by finding a straight line (or hyperplane) that best separates them. It works by identifying a direction that maximizes the difference between the groups' average values while minimizing the variation within each group along that line. Essentially, it transforms complex data into a single dimension where the groups are most distinguishable, making it easier to classify new data points. This technique is widely used in pattern recognition and machine learning for tasks like face recognition and medical diagnosis.