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Should be similar to that of perf.mint.splsda using the same hyperparameters:
mint.splsda.res= mint.splsda(X=X, Y=Y, study=study, ncomp=2,
keepX=tune.mint$choice.keepX)
mint.splsda.res# lists useful functions that can be used with a MINT objectperf.mint= perf.mint.splsda(mint.splsda.res, progressBar=FALSE, dist='max.dist')
plot(perf.mint)
A possible solution is to ensure LOGOCV and perf.mint.splsda (and possibly other perf functions) call the same internal that does dev/test on studies and then make sure the outputs are identical as well.
The text was updated successfully, but these errors were encountered:
Homogenised way in which `tune.mint.splsda()` and `perf.mint.splsda()` calculate BER, such that now both use weighted average of BER across studies. Added unit tests to ensure this homogenity
The performance of the
tune.mint.splsda
model at optimum hyperparameters:Should be similar to that of
perf.mint.splsda
using the same hyperparameters:A possible solution is to ensure
LOGOCV
andperf.mint.splsda
(and possibly otherperf
functions) call the same internal that does dev/test on studies and then make sure the outputs are identical as well.The text was updated successfully, but these errors were encountered: