Compare Bagging and Boosting for Claims Risk

Business Context

BrightShield Insurance wants a simple, evidence-based explanation of when to use bagging vs boosting for a tabular risk model. The analytics team currently uses single decision trees to predict whether an auto insurance claim will become high-cost, but performance is unstable across retraining runs.

Dataset

You are given a historical claims dataset for binary classification.

• Rows: 120,000 claims, 26 features
• Target: high_cost_claim = 1 if final payout exceeds $15,000, else 0
• Class balance: 18% positive, 82% negative
• Missing data: ~7% missing in repair estimates, ~4% missing in annual mileage, <1% elsewhere

Success Criteria

A strong solution should:

• clearly explain the practical difference between bagging and boosting in simple terms,
• implement and compare at least one bagging model and one boosting model,
• improve over a single decision tree on out-of-sample performance,
• provide a recommendation for production given interpretability and latency constraints.

Constraints

• Batch scoring runs hourly on up to 50,000 claims
• Underwriting stakeholders want a simple explanation of model behavior
• Training must be reproducible and finish within a standard laptop or small VM environment

Deliverables

1. Explain bagging vs boosting in plain language for a non-technical stakeholder.
2. Train a baseline decision tree, a bagging model, and a boosting model.
3. Compare models using cross-validated and test-set metrics.
4. Summarize bias-variance tradeoffs and failure modes for each approach.
5. Recommend which approach to deploy for this claims dataset and why.