Compare bagging vs boosting on imbalanced data
Overview
This question evaluates competency in ensemble model selection (bagging vs boosting), handling extreme class imbalance, metric selection, hyperparameter tuning, time-aware validation, and failure-mode diagnosis for large-scale, time-ordered binary classification, and is commonly asked because it probes bias–variance trade-offs, robustness to label noise on the minority class, and practical evaluation and leakage-avoidance concerns in Machine Learning. It assesses both conceptual understanding of algorithmic trade-offs and statistical robustness as well as practical application skills such as designing time-blocked cross-validation, selecting appropriate metrics for imbalanced data, specifying hyperparameter grids, and interpreting diagnostic plots.
Fraud Detection on 10M Time-Ordered Transactions (0.5% Fraud)
You are building a binary classifier to detect 0.5% fraudulent events among 10,000,000 time-ordered transactions with 300 features (100 numeric, 200 one-hot). You must choose between a bagged Random Forest and a Gradient Boosting model (e.g., XGBoost or LightGBM).
Address the following:
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Model choice
- Which would you try first (Random Forest vs Gradient Boosting) and why?
- Reference: bias–variance trade-offs, margins, and how each method reacts to label noise on the minority class.
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Class imbalance and metric
- How will you handle class imbalance (class weights vs downsampling the majority vs SMOTE/SMOTE-ENN)?
- Which primary metric will you optimize (PR-AUC vs ROC-AUC) and why?
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Hyperparameter grids (initial, concrete values and expected effects)
- Random Forest: n_estimators, max_depth, max_features, class_weight.
- Gradient Boosting (XGBoost/LightGBM): learning_rate, max_depth, min_child_weight, subsample, colsample_bytree, scale_pos_weight.
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Validation plan (avoid leakage)
- Describe a time-based blocked cross-validation plan and grouping by user_id.
- How you will use early stopping (for boosting) or OOB estimates (for RF).
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Failure modes
- Two cases where boosting would underperform bagging on this task, and how you would diagnose them with plots/diagnostics.
Solution
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