Week 3: Model Evaluation and Validation Techniques

Overview

This week covers essential concepts in model evaluation, statistical validation, and performance metrics. Students will learn how to properly assess model performance and understand the importance of robust validation techniques.

Learning Objectives

• Understand the limitations and proper use of p-values
• Master cross-validation techniques and their applications
• Implement proper train-validation-test splits
• Calculate and interpret various performance metrics
• Apply appropriate evaluation methods for different scenarios

1. Statistical Validation

• P-values and Statistical Testing
  • Understanding significance
  • Limitations and common misinterpretations
  • Multiple testing problems
• Data Splitting Strategies
  • Train-validation-test splits
  • Hold-out methods
  • Repeated hold-out
  • Time-series considerations

2. Cross-Validation Techniques

• Basic Cross-Validation
  • K-fold cross-validation
  • Leave-one-out cross-validation
  • Repeated cross-validation
• Advanced Methods
  • Stratified cross-validation
  • Group cross-validation
  • Nested cross-validation
  • Time-series cross-validation

3. Performance Metrics

• Classification Metrics
  • Confusion matrix
  • Precision and recall
  • F1 score
  • Accuracy and its limitations
• ROC Analysis
  • ROC curves
  • AUC-ROC
  • Precision-Recall curves
  • Operating point selection

4. Advanced Evaluation Concepts

• Cost-Sensitive Evaluation
  • Cost matrices
  • Class imbalance handling
• Bias-Variance Analysis
  • Understanding the trade-off
  • Learning curves
  • Validation curves
  • Error analysis