QDA works by modeling the probability distribution of each class separately. It calculates the covariance matrix for each class and uses these matrices to construct quadratic decision boundaries. The key steps include: 1. Estimating the mean and covariance matrix for each class. 2. Calculating the posterior probabilities for each class given the input data. 3. Classifying the data point to the class with the highest posterior probability.
