Quadratic Discriminant Analysis (QDA)

Quadratic Discriminant Analysis (QDA) is a statistical method used to classify data points into different groups based on their features. It assumes each group has its own unique pattern of data variability, represented by different shapes (ellipses) in the feature space. By analyzing these patterns, QDA calculates the likelihood that a new data point belongs to each group. It then assigns the point to the group with the highest probability. Unlike simpler methods, QDA accounts for differences in data spread within each class, making it effective when the classes have distinct variability patterns.