Hi all,

If I run LDA on the same data (2-class classification) with default(no
priors specified in the lda function) vs. "prior=c(0.5, 0.5)", the results
are different.

The (0.5, 0.5) priors give better 1-classify-to-1 rate, and the proportional
priors(default, no priors specified in the lda function) give better
0-classify-to-0 rate, for both training and testing data sets.

However, if I run QDA on the same data (2-class classification) with
default(no priors specified in the lda function) vs. "prior=c(0.5, 0.5)",
the results are the same,

i.e. the confusion tables are completely the same for two types of priors, I
even tried "qda" function with "prior=c(0.3, 0.7)" and other values, the
confusion tables are still the same...

What might be the problem?

Thanks a lot!

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