LDA, or Linear Discriminant Analysis, is a statistical method used for dimensionality reduction and classification that focuses on finding a linear combination of features that best separates two or more classes. It maximizes the distance between the means of different classes while minimizing the variation within each class, effectively creating a new feature space where classification can be more straightforward. LDA is particularly effective when the data is normally distributed and when classes have similar covariance structures.
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