ml-rigor
CommunityRigorous ML eval: baselines, CV, interpretation.
Data & Analytics#cross-validation#interpretation#ml#model-evaluation#baselines#permutation-importance#leakage-prevention
AuthorYeachan-Heo
Version1.0.0
Installs0
System Documentation
What problem does it solve?
Enforces baseline comparisons, cross-validation, interpretation, and leakage prevention for ML pipelines.
Core Features & Use Cases
- Baseline-driven evaluation: require comparison to a dummy or simple model before claiming progress.
- Comprehensive cross-validation: report mean and std across stratified folds, with confidence intervals.
- Model interpretation: provide permutation importance and SHAP analyses to explain predictions.
- Leakage prevention & error analysis: detect data leakage, slice performance by segments, and analyze failure modes.
Quick Start
Load your dataset, define a model, and run the ml-rigor workflow to generate baselines, CV metrics, and interpretation artifacts ready for reporting.
Dependency Matrix
Required Modules
None requiredComponents
Standard package💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: ml-rigor Download link: https://github.com/Yeachan-Heo/My-Jogyo/archive/main.zip#ml-rigor Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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