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  1. PUBDEV-3971

Add nfolds argument to Stacked Ensemble to enable cross-validation

    Details

    • Type: New Feature
    • Status: Open
    • Priority: Major
    • Resolution: Unresolved
    • Affects Version/s: None
    • Fix Version/s: None
    • Component/s: StackedEnsemble
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    • CustomerVisible:
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      Description

      Just like all the other H2O algos, we should be able to perform k-fold cross-validation of the ensemble. To do a full cross-validation of the base model training + ensemble process, its a very computationally heavy operation. I am looking into the idea of just cross-validating the metalearner instead and reporting those metrics/fits. This is actually the only thing that makes sense to support anyway through our current API which expects to be given the already-cross-validated base models (containing cv preds).

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            • Assignee:
              rpeck Raymond Peck
              Reporter:
              erin Erin LeDell
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              • Created:
                Updated: