Discriminant Analysis

Discriminant Analysis is a statistical method used to determine which variables discriminate between two or more naturally occurring groups. It is a supervised learning technique that is commonly used in machine learning and pattern recognition. The goal of discriminant analysis is to find a linear combination of features that characterizes or separates two or more classes of objects or events. This method is used to classify new observations into one of the pre-defined classes based on the linear combination of features. Discriminant analysis assumes that the data is normally distributed and that the covariance matrices of the different classes are equal. It can be used for both binary and multi-class classification problems.
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