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jiachengwang-punch

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   Small Sample Analysis · jiachengwang-punch bundle

   End-to-end methodology for supervised machine learning on small datasets (typically 30-200 samples) where standard "throw XGBoost at it" approaches fail. Use this skill whenever the user is building a predictive model on a small dataset, especially when sample-to-feature ratios are tight, when interpretability matters as much as accuracy, when the user needs to justify model choices to non-technical stakeholders, or when they need a rigorous "diagnose-improve-verify" workflow rather than just a final model. Trigger this even if the user only asks for a specific piece (e.g. "help me pick features", "validate this model"), since small-sample problems require the full methodology to avoid silent overfitting. Also trigger for store-selection / site-selection problems, B2B sales analytics, biomedical studies, A/B test analysis with limited cohorts, and any "we only have N stores/patients/experiments and need to predict Y" scenario.
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