This is a stable release of a work-in-progress under active development. We make no guarantees about the quality of this software. To the best of our knowledge, we have followed the machine learning “best practices” when developing this software, but if you know better than us, please let us know on GitHub.
top
argument greater than the number of total number of features available in a training set, exprso will automatically use all features instead.ExprsPipeline
model extraction, if you supply an \(x\) number of top models to the top
argument 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 pipeFilter
first, then call buildEnsemble
on the pipeFilter
results after inspecting them manually.plCV
provides an overly-optimistic metric of classifier performance that should never get published. However, the results of plCV
do have relative validity, so it is fine to use them to choose parameters.splitSample
method builds the training and validation sets by randomly sampling all subjects in an ExprsArray
object. However, splitSample
is 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.