🔍 Model Comparison

As you build and train different machine learning models, one of the most valuable skills is knowing how to compare them effectively.

This section teaches you how to:

• ✅ Evaluate models using consistent metrics like accuracy, precision, recall, F1, and AUC
  
• 📊 Visualize performance through boxplots, ROC curves, and heatmaps
  
• 🔎 Identify the strengths and trade-offs of each algorithm
  
• 🧠 Make informed decisions on which model to deploy or refine

💡 No single metric tells the whole story. A good model for one problem might fail in another. This section helps you think critically about your models — not just run them.

We’ll compare models like Logistic Regression, SVM, Random Forest, and Naive Bayes on real datasets and learn how to spot the best fit for your task.