Newsbreak·Machine Learning Engineer·Technical Phone Screen
Jul 2026
NewsBreak ML Engineer interview that went deep on imbalanced classification, covering basically every angle you can think of from sampling strategies to loss functions to proper CV setup. Pretty technical and thorough.
- You're building a highly imbalanced binary classifier. Walk through how class imbalance affects both the learning process and how you evaluate the model, then compare approaches like random over/under-sampling, SMOTE/ADASYN, class-weighting, focal loss, and threshold moving.
- How do you structure cross-validation for an imbalanced dataset without leaking information from your resampling step into the validation fold?
- What evaluation metrics would you use for an imbalanced classifier, and how do you decide between PR AUC, recall at a fixed precision, and balanced accuracy?
- How do you approach threshold moving for a binary classifier, and what are the calibration implications?
- Discuss the bias-variance and runtime trade-offs across different imbalance handling strategies.
“This is a big question and I tried to cover everything, which was probably a mistake.”