ML Model Evaluation Expert
Evaluates machine learning models with rigorous methodology and statistical analysis. Covers metrics selection, cross-validation strategies, bias detection, A/B testing, confusion matrices, ROC/AUC analysis, and comprehensive evaluation reporting.
SupaScore
81Best for
- ▸Selecting appropriate evaluation metrics for imbalanced classification problems
- ▸Designing nested cross-validation strategies to avoid optimistic bias in hyperparameter tuning
- ▸Conducting statistical significance tests between competing model performances
- ▸Building comprehensive evaluation reports with bias detection and fairness analysis
- ▸Setting up A/B testing frameworks for model performance comparison in production
What you'll get
- ●Structured evaluation protocol with nested CV design, statistical tests (McNemar's, paired t-test), and bias analysis across protected attributes
- ●Comprehensive metrics dashboard with ROC/PR curves, calibration plots, residual analysis, and confidence intervals via bootstrap
- ●A/B testing framework specification with sample size calculations, success criteria, and guardrail metrics for production model comparison
Not designed for ↓
- ×Training or building machine learning models from scratch
- ×Data preprocessing and feature engineering tasks
- ×Model deployment and infrastructure setup
- ×Business strategy or ROI analysis of ML projects
Trained ML models with validation datasets, specific problem type (classification/regression/ranking), and business context including fairness requirements.
Comprehensive evaluation framework with statistical analysis, bias assessment, confidence intervals, and actionable recommendations for model selection.
Evidence Policy
Enabled: this skill cites sources and distinguishes evidence from opinion.
Research Foundation: 8 sources (2 official docs, 5 academic, 1 industry frameworks)
This skill was developed through independent research and synthesis. SupaSkills is not affiliated with or endorsed by any cited author or organisation.
Version History
Initial version
Prerequisites
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Works well with
Need more depth?
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Common Workflows
ML Model Development & Evaluation Pipeline
Complete pipeline from model training through rigorous evaluation to experiment tracking and comparison
Production ML Deployment with Bias Monitoring
Comprehensive evaluation including fairness assessment before deployment with ongoing monitoring
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