topargument greater than the number of total number of features available in a training set, exprso will automatically use all features instead.
ExprsPipelinemodel extraction, if you supply an $x$ number of top models to the
topargument greater than the total number of models available in a filtered cut of models, exprso will automatically use all models instead. If you are concerned about this default behavior, call
pipeFilterfirst, then call
pipeFilterresults after inspecting them manually.
plCVprovides an overly-optimistic metric of classifier performance that should never get published. However, the results of
plCVdo have relative validity, so it is fine to use them to choose parameters.
splitSamplemethod builds the training and validation sets by randomly sampling all subjects in an
splitSampleis not truly random; it iteratively samples until at least one of every class appears in the test set. This rule makes it easier to run analyses and interpret results, but requires caution when articulating in a report how you chose the test set.
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