Machine Learning Rigor Patterns

When to Use

Load this skill when building machine learning models. Every ML pipeline must demonstrate:

• Baseline comparison: Beat a dummy model before claiming success
• Cross-validation: Report variance, not just a single score
• Interpretation: Explain what the model learned
• Leakage prevention: Ensure no future information leaks into training

Quality Gate: ML findings without baseline comparison or cross-validation are marked as "Exploratory" in reports.

1. Baseline Requirements

Every model must be compared to baselines. A model that can't beat a dummy classifier isn't learning anything useful.