9  Model Evaluation and Analysis

This chapter provides a unified framework for evaluating and analyzing models across multiple AI paradigms: Machine Learning (ML), Deep Learning (DL), Deep Reinforcement Learning (DRL), Large Language Models (LLMs), and Computer Vision (CV). The goal is to assess performance, robustness, efficiency, and interpretability, providing actionable insights for model improvement and real-world deployment.

9.1 ML Model Evaluation & Analysis

Applies to classical models like Decision Trees, Random Forests, SVMs, and Gradient Boosting for structured data.

The Evaluation Metrics are :

9.1.1 Calculate Mean Absolute Error (MAE)

Analysis

• Use cross-validation for unbiased performance estimation.
• Visualize confusion matrix and feature importances.
• Evaluate bias–variance tradeoff with learning curves.
• Assess scalability for increasing dataset sizes.

Interpretation: - Consistent performance across folds with stable feature importance indicates generalizatio