XGBoost, DP, and a Fraud-Detection System Design That Didn't Land

Interview time: February this year. Department: e-commercial team. Round 1: Self-introduction, walked through my projects. A few classic ML Q&A: Q: Why is XGBoost less prone to overfitting than a single decision tree? A: Because it's a gradient-boosting ensemble method (many weak-learner trees), it adds regularization (L1/L2, tree complexity penalties), a learning rate (shrinkage), row/column sub…

Shared by a candidate · reported Mar 2026. Interview formats change; use it as a guide, not a script.