LDA assumes heterogeneity among class variances, meaning they each share a single covariance matrix, while QDA allows for each class to have its own variance. Thus, QDA provides additional flexibility for learning non-linear decision boundaries. It is generally recommended to try both LDA and QDA on a dataset and use cross validation to determine which performs best.
What differentiates Linear Discriminant Analysis (LDA) from Quadratic Discriminant Analysis (QDA)?
LDA assumes heterogeneity among class variances, meaning they each share a single covariance matrix, while QDA allows for each class to have its own variance. Thus, QDA provides additional flexibility for learning non-linear decision boundaries. It is generally recommended to try both LDA and QDA on a dataset and use cross validation to determine which performs best.
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