imbalance

The interviewer wants to see whether you choose metrics to fit the problem or just recite definitions. The signal that counts is recognizing when accuracy and even AUC mislead, and linking each metric to a decision.

Plenty of real problems hand you confirmed positives but never confirmed negatives, only unlabeled data. The shortcut everyone grabs quietly biases the model. What shows depth is naming the regime and its fix. Here is the answer.

Some models are multiclass out of the box; others need a wrapper to extend a binary classifier to K classes. What shows depth is knowing the three strategies and exactly when each one wins. Here is the answer.

A 92% aggregate accuracy can mask 60% on the segment that matters most. Averages are exactly where these failures stay buried. Here is how to surface them and the menu of fixes that actually map to the cause.

The trap is swapping the classifier for an LLM. The real-time, imbalance, and adversarial constraints stay put. The signal is a hybrid: a fast calibrated model scores inline, LLMs investigate the gray zone off the hot path.

Spam detection is adversarial, imbalanced classification under a low-latency bar. The interviewer is watching whether you set the threshold by cost asymmetry and rely on signals spammers cannot fake. Here is the design.

A high-AUC churn model that changes no behavior is worthless. The strong answer nails the label definition, chooses classification vs survival deliberately, and is judged on retention uplift, not accuracy.