Linear Discriminant Analysis

Linear Discriminant Analysis (LDA) is a statistical method used to classify data by finding the best way to separate different groups. It works by identifying a line or a balance point that maximizes the difference between the groups’ averages while minimizing overlap. Imagine grouping similar items and drawing a line between categories to classify new items accurately. LDA assumes data from each group follows a normal distribution and has similar spread, making it effective for distinguishing categories based on their features. It is widely used in face recognition, medical diagnosis, and other classification tasks.