Closed saraemoore closed 6 years ago
I haven't had a chance to check out yet, but would it be possible to create a very simple test for this function?
Good idea. I'll do that now and update the PR.
I looked through the failed checks briefly, and I don't believe any of these are attributable to my changes. The build failures seem to be mostly (entirely?) due to problems with dependencies like BioC, quadprog, nlopt, etc.
Thanks, I don't foresee a problem with this change, it just wasn't a use case when SampleSplit was created. One minor note, can you leave the version number in the Description, but add a comment in the inst/NEWS file with the description of the change?
Cool.
Changes now made. I updated the date in both DESCRIPTION and NEWS.
I'm doing something akin to leave-one-out CV, but with observation-specific weights and columns of X. I'm currently using this fork's version of
SampleSplitSuperLearner()
to accomplish this by specifying a single row of X as the validation sample. All other solutions that I've come up with thus far either require unnecessary additional sample splitting (for example, passing in the validation observation vianewX
) or unnecessary duplication of code (for example, writing a customGridLearner()
, like aSuperLearner()
that doesn't cross-validate).Previously,
SampleSplitSuperLearner()
needed at least two observations to be in the validation set if specific row numbers of X were provided. I modifiedSampleSplitSuperLearner()
to accept only one specific observation as the validation sample, while still maintaining the ability to pass in a proportion of the sample to use as the training set instead of row numbers. Let me know if you foresee any issues with this modification.