But there is a trade-off: if LDA’s assumption that the the predictor variable share a common variance across each Y response class is badly off, then LDA can suffer from high bias. This can potentially lead to improved prediction performance. Īlso, when considering between LDA & QDA its important to know that LDA is a much less flexible classifier than QDA, and so has substantially lower variance. A simple rule of thumb is to use LDA & QDA on data sets where. Furthermore, its important to keep in mind that performance will severely decline as p approaches n.
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