🤖Machine Learning
✅
Model Evaluation & Validation
Learn to properly evaluate ML models and avoid common pitfalls like overfitting
Prerequisites
🌱
Beginner
BeginnerFundamentals of model evaluation
What to Learn
- •Train/validation/test splits
- •K-fold cross-validation
- •Bias-variance tradeoff intuition
- •Overfitting and underfitting
- •Learning curves analysis
Resources
- 📚Scikit-learn model selection guide
- 📚Kaggle Learn: Model Validation
- 📚ML Mastery evaluation tutorials
🌿
Intermediate
IntermediateRobust evaluation practices
What to Learn
- •Stratified and grouped cross-validation
- •Time series cross-validation
- •Evaluation metrics deep dive
- •Statistical significance testing
- •A/B testing for ML models
Resources
- 📚Trustworthy Online Experiments
- 📚Statistical tests for ML papers
- 📚Real-world evaluation case studies
🌳
Advanced
AdvancedProduction evaluation challenges
What to Learn
- •Distribution shift detection
- •Online evaluation and bandits
- •Fairness metrics and auditing
- •Uncertainty estimation methods
- •Model monitoring and drift detection
Resources
- 📚ML Ops best practices
- 📚Fairness in ML research
- 📚Concept drift detection papers