class imbalance

Imbalance turns up in fraud, churn, and abuse, and the naive answer (oversample, done) leaks data and inflates offline metrics. The signal is favoring cost-sensitive learning, fixing the metric, and resampling correctly. Here is the full toolkit with its tradeoffs.

Severe class imbalance, a tight latency budget, and an adversary who keeps adapting. What interviewers watch for: treating imbalance honestly, setting the operating point from costs, and building for the feedback loop. This covers the end-to-end design.

The positive class runs about 1 in 1,000, your labels show up late and noisy, and the instant you ship a model the adversary starts probing it. This is the uncommon ML problem where the data actively fights back, so the design hinges on labels, latency, and enforcement cost as much as on the classifier.

Fraud is well under 1% of transactions, the labels land weeks late as chargebacks, and the fraudsters deliberately adapt to your model. Optimizing accuracy hands you a model that approves everything. The interview is about dollars-at-risk thresholding, label delay, and a review queue with a fixed headcount.