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dianaprior

@dianaprior source repo

4 published skills

  1. ▌
    Tabpfn Core · dianaprior bundle
    Shared identity, behavior rules, workflow principles, and project conventions for TabPFN tabular competition skills. Referenced by tabpfn-classify, tabpfn-regress, and tabpfn-explore — not invoked directly.
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  2. ▌
    Tabpfn Explore · dianaprior
    EDA, data profiling, adversarial validation, preprocessing checks, CV scheme setup, and API budget verification for tabular Kaggle competitions. Run at the start of every new competition before any modeling.
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  3. ▌
    Tabpfn Regress · dianaprior
    Run a TabPFN regression baseline, generate the first submission, then optimize with GBT ensembles and regression-specific post-processing (clipping, target transforms, rank blending). Use after tabpfn-explore has prepared the data and CV folds.
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    Tabpfn Classify · dianaprior
    Run a TabPFN classification baseline, generate the first submission, rapidly probe features, then optimize with GBT ensembles, threshold tuning, and calibration. Use after tabpfn-explore has prepared the data and CV folds.
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