Model Evaluation

Statistical Evaluation Methods

• What you Need to Know

  • Cross-Validation Techniques

    • K-fold cross-validation and stratified sampling
    • Leave-one-out and leave-p-out cross-validation
    • Time series cross-validation for temporal data
    • Resources:
      • Cross-Validation - Scikit-learn comprehensive CV guide
      • Time Series CV - Time series validation techniques
      • Nested Cross-Validation - Model selection and evaluation

  • Bootstrap Methods

    • Bootstrap sampling and confidence interval estimation
    • Bootstrap aggregating (bagging) for model evaluation
    • Bias-corrected and accelerated (BCa) bootstrap
    • Resources:
      • Bootstrap Methods - ESL bootstrap chapter
      • Bootstrap Confidence Intervals - Statistical bootstrap methods
      • Scikit-learn Bootstrap - Bootstrap implementation

  • Statistical Significance Testing

    • Paired t-tests for model comparison
    • McNemar's test for classification models
    • Wilcoxon signed-rank test for non-parametric comparisons
    • Resources:
      • Statistical Tests for ML - Model comparison tests
      • McNemar's Test - Classification model comparison
      • Scipy Statistical Tests - Statistical testing functions

Performance Metrics and Analysis

• What you Need to Know

  • Classification Metrics

    • Precision, recall, F1-score, and their micro/macro averages
    • ROC curves, AUC, and precision-recall curves
    • Matthews Correlation Coefficient and balanced accuracy
    • Resources:
      • Classification Metrics - Comprehensive classification evaluation
      • ROC and AUC - Google's ROC curve tutorial
      • Precision-Recall Curves - PR curve analysis

  • Regression Metrics

    • Mean Squared Error (MSE) and Root Mean Squared Error (RMSE)
    • Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE)
    • R-squared, adjusted R-squared, and explained variance
    • Resources:
      • Regression Metrics - Regression evaluation guide
      • Understanding R-squared - R-squared interpretation
      • Regression Evaluation - Comprehensive regression metrics

  • Multi-class and Multi-label Evaluation

    • One-vs-rest and one-vs-one evaluation strategies
    • Hamming loss and subset accuracy for multi-label
    • Label ranking and coverage error metrics
    • Resources:
      • Multi-class Classification - Multi-class evaluation strategies
      • Multi-label Metrics - Multi-label evaluation
      • Multi-label Classification Survey - Comprehensive multi-label methods

Model Selection and Comparison

• What you Need to Know

  • Information Criteria

    • Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC)
    • Model complexity and parsimony principles
    • Cross-validation vs information criteria trade-offs
    • Resources:
      • Model Selection Criteria - ESL model selection chapter
      • AIC vs BIC - Information criteria comparison
      • Information Theory - MacKay's information theory book

  • Learning Curves and Validation Curves

    • Training and validation error analysis
    • Bias-variance decomposition visualization
    • Optimal model complexity identification
    • Resources:
      • Learning Curves - Scikit-learn learning curves
      • Validation Curves - Parameter validation curves
      • Bias-Variance Analysis - Visual bias-variance explanation

  • Model Ensemble Evaluation

    • Individual model vs ensemble performance
    • Diversity measures and ensemble effectiveness
    • Stacking and blending evaluation strategies
    • Resources:
      • Ensemble Evaluation - Ensemble performance assessment
      • Diversity in Ensembles - Ensemble diversity measures
      • Stacking Evaluation - Multi-level ensemble evaluation

Robustness and Generalization Analysis

• What you Need to Know

  • Adversarial Robustness Testing

    • Adversarial example generation (FGSM, PGD, C&W)
    • Robustness metrics and certified defenses
    • Adversarial training evaluation
    • Resources:
      • Adversarial Examples - Intriguing properties of neural networks
      • Adversarial Robustness Toolbox - IBM's adversarial ML library
      • Certified Defenses - Provable adversarial robustness

  • Distribution Shift and Domain Adaptation

    • Covariate shift and concept drift detection
    • Domain adaptation evaluation metrics
    • Out-of-distribution detection methods
    • Resources:
      • Dataset Shift - Comprehensive dataset shift analysis
      • Domain Adaptation - Domain adaptation survey
      • Out-of-Distribution Detection - OOD detection methods

  • Fairness and Bias Evaluation

    • Demographic parity and equalized odds
    • Individual fairness and counterfactual fairness
    • Bias detection and mitigation evaluation
    • Resources:
      • Fairness in Machine Learning - Comprehensive fairness guide
      • AI Fairness 360 - IBM's fairness toolkit
      • Fairness Metrics - Survey of fairness definitions

Experimental Design for ML

• What you Need to Know

  • A/B Testing for ML Models

    • Online experimentation and statistical power
    • Multi-armed bandit approaches
    • Causal inference in model evaluation
    • Resources:
      • A/B Testing Guide - Microsoft's experimentation platform
      • Multi-Armed Bandits - Bandit algorithms for online learning
      • Causal Inference - Causal inference: The Mixtape

  • Randomized Controlled Trials

    • Experimental design principles for ML evaluation
    • Treatment assignment and randomization strategies
    • Confounding variables and control methods
    • Resources:
      • Experimental Design - ESL experimental design chapter
      • Randomized Experiments - RCT design principles
      • Field Experiments - Field experimentation guide

Performance Monitoring and Drift Detection

• What you Need to Know

  • Model Performance Monitoring

    • Real-time performance tracking systems
    • Performance degradation detection
    • Automated retraining triggers
    • Resources:
      • ML Monitoring - ML model monitoring guide
      • MLflow Model Registry - Model lifecycle management
      • Evidently AI - ML monitoring and testing

  • Data Drift Detection

    • Statistical tests for distribution changes
    • Kolmogorov-Smirnov and chi-square tests
    • Population Stability Index (PSI) monitoring
    • Resources:
      • Data Drift Detection - Alibi Detect drift detection
      • Statistical Tests for Drift - Concept drift detection
      • PSI Monitoring - Population stability index

  • Concept Drift and Model Adaptation

    • Gradual vs sudden concept drift detection
    • Adaptive learning algorithms
    • Online learning and model updating strategies
    • Resources:
      • Concept Drift Survey - Comprehensive concept drift analysis
      • Online Learning - Online machine learning survey
      • River Online ML - Online machine learning library

Interpretability and Explainability Evaluation

• What you Need to Know

  • Global Model Interpretability

    • Feature importance ranking and stability
    • Partial dependence plots and accumulated local effects
    • Global surrogate model fidelity
    • Resources:
      • Interpretable ML - Comprehensive interpretability guide
      • SHAP Global Explanations - Global SHAP analysis
      • Permutation Importance - Feature importance evaluation

  • Local Explanation Quality

    • LIME and SHAP explanation consistency
    • Counterfactual explanation evaluation
    • Human-interpretable explanation assessment
    • Resources:
      • LIME Evaluation - Local interpretable explanations
      • Explanation Evaluation - Evaluating explanation methods
      • Human-AI Interaction - Human evaluation of explanations

Benchmarking and Reproducibility

• What you Need to Know

  • Benchmark Dataset Evaluation

    • Standard benchmark performance comparison
    • Cross-dataset generalization assessment
    • Benchmark limitations and biases
    • Resources:
      • ML Benchmarks - Papers with Code benchmark datasets
      • OpenML - Open machine learning platform
      • Benchmark Bias - Systematic benchmark evaluation

  • Reproducibility and Experimental Validation

    • Reproducible research practices
    • Statistical significance and effect sizes
    • Replication studies and meta-analysis
    • Resources:
      • Reproducible Research - Johns Hopkins reproducibility course
      • ML Reproducibility - Reproducibility crisis in ML
      • Papers with Code - Code availability for research

  • Competition and Challenge Evaluation

    • Kaggle competition evaluation strategies
    • Academic challenge participation
    • Leaderboard overfitting and validation
    • Resources:
      • Kaggle Competition Guide - Competition participation strategies
      • Competition Evaluation - Machine learning competition analysis
      • Leaderboard Overfitting - Challenges in competition evaluation

Ready for Advanced Techniques? Continue to Module 5: Advanced Techniques to master cutting-edge research methods, specialized algorithms, and emerging ML paradigms.