Model Evaluation Project

🎯 Project: Complete Model Evaluation

This project will help you apply everything you've learned about model evaluation. You'll evaluate multiple models, compare their performance, identify overfitting/underfitting, and use cross-validation to get reliable estimates.

Proper model evaluation is crucial for building reliable ML systems. This project gives you hands-on experience with the complete evaluation workflow.

Evaluating Multiple Models

In practice, you'll compare multiple models. Here's how to evaluate them systematically:

compare_models.py
# Comparing Multiple Models

# Model performance results
models = {
    'Model A': {'train_acc': 0.95, 'test_acc': 0.72},
    'Model B': {'train_acc': 0.85, 'test_acc': 0.83},
    'Model C': {'train_acc': 0.60, 'test_acc': 0.58}
}

print("Model Comparison:")
print("=" * 50")
for name, perf in models.items():
    gap = perf['train_acc'] - perf['test_acc']
    print(f"\n{name}:")
    print(f"  Train: {perf['train_acc']:.0%}")
    print(f"  Test: {perf['test_acc']:.0%}")
    print(f"  Gap: {gap:.0%}")
    
    if gap > 0.15:
        print("  Status: OVERFITTING")
    elif perf['test_acc'] < 0.70:
        print("  Status: UNDERFITTING")
    else:
        print("  Status: GOOD FIT")

print("\nBest Model: Model B (good test performance, small gap)")

Using Cross-Validation for Evaluation

Cross-validation gives you more reliable performance estimates:

cv_evaluation.py
# Cross-Validation Evaluation

# Simulated cross-validation results (5 folds)
model_cv_scores = {
    'Model A': [0.70, 0.72, 0.71, 0.73, 0.72],
    'Model B': [0.82, 0.84, 0.83, 0.85, 0.83],
    'Model C': [0.57, 0.59, 0.58, 0.60, 0.58]
}

print("Cross-Validation Results:")
print("=" * 50")

for name, scores in model_cv_scores.items():
    mean_score = sum(scores) / len(scores)
    variance = sum((s - mean_score) ** 2 for s in scores) / len(scores)
    std_score = variance ** 0.5
    
    print(f"\n{name}:")
    print(f"  CV Scores: {[f'{s:.2%}' for s in scores]}")
    print(f"  Mean: {mean_score:.2%}")
    print(f"  Std Dev: {std_score:.2%}")
    print(f"  Range: {min(scores):.2%} - {max(scores):.2%}")

print("\nCV provides:")
print("  - More reliable performance estimate")
print("  - Shows model stability (lower std = more stable)")
print("  - Better model comparison")

Complete Evaluation Workflow

A complete evaluation includes multiple metrics and techniques:

complete_evaluation.py
# Complete Model Evaluation Workflow

print("Complete Evaluation Checklist:")
print("=" * 50")

print("\n1. Basic Metrics:")
print("   ✓ Accuracy (classification)")
print("   ✓ Precision, Recall, F1-score")
print("   ✓ MAE, RMSE, R² (regression)")

print("\n2. Train-Test Comparison:")
print("   ✓ Compare training vs test performance")
print("   ✓ Identify overfitting/underfitting")
print("   ✓ Calculate performance gap")

print("\n3. Cross-Validation:")
print("   ✓ Use k-fold cross-validation")
print("   ✓ Calculate mean and std of scores")
print("   ✓ Assess model stability")

print("\n4. Confusion Matrix:")
print("   ✓ Build confusion matrix")
print("   ✓ Analyze TP, TN, FP, FN")
print("   ✓ Identify error patterns")

print("\n5. Model Comparison:")
print("   ✓ Compare multiple models")
print("   ✓ Select best model")
print("   ✓ Document findings")

Exercise: Complete Model Evaluation Project

Complete the exercise on the right side:

Task 1: Evaluate three models with train/test accuracy
Task 2: Identify which model has overfitting/underfitting
Task 3: Calculate cross-validation scores for each model
Task 4: Compare models and select the best one
Task 5: Calculate confusion matrix for the best model

Write your code to complete this comprehensive model evaluation project!

💡 Project Tips

Break the project into smaller tasks. Complete and test each part before moving to the next. Don't try to do everything at once—iterative development leads to better results!

🎉

Lesson Complete!

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