Business Context
TechCorp, a leading provider of e-commerce solutions, is evaluating different ensemble learning techniques to improve the accuracy of their product recommendation system. With millions of transactions per day, understanding the nuances of model performance is crucial for maintaining customer satisfaction and increasing sales.
Dataset Description
| Feature Group | Count | Examples |
|---|
| User Features | 10 | user_id, age, gender, location |
| Product Features | 15 | product_id, category, price, ratings |
| Interaction Features | 20 | purchase_history, click_through_rate, time_spent |
- Size: 1.2 million records with 45 features
- Target: Categorical — product purchased (1) vs not purchased (0)
- Class balance: Approximately 5% positive (purchased), 95% negative (not purchased)
- Missing data: 10% missing in interaction features due to incomplete user sessions
Success Criteria
- Achieve at least 90% accuracy on a held-out test set.
- Maintain a precision of at least 70% to ensure quality recommendations.
- Provide a clear explanation of the differences between bagging and boosting techniques, including their advantages and disadvantages.
Constraints
- The model must be interpretable enough for the product team to understand the decision-making process.
- Training time should not exceed 2 hours on standard hardware.
Deliverables
- A detailed comparison of bagging and boosting techniques, including when to use each.
- Implementation of both techniques using a sample dataset.
- Evaluation metrics and analysis of model performance for both methods.
