imbalance

The interviewer is testing whether you pick metrics for the problem or recite definitions. The real signal is knowing when accuracy and even AUC lie, and tying each metric to a decision.

Many real problems give you confirmed positives but never confirmed negatives, only unlabeled data. The shortcut everyone reaches for quietly biases the model. The signal is naming the regime and its fix. Here is the answer.

Some models are natively multiclass; others need a wrapper to stretch a binary classifier to K classes. The signal is knowing the three strategies and exactly when each one wins. Here is the answer.

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

The trap is replacing the classifier with an LLM. The real-time, imbalance, and adversarial constraints do not go away. 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 for whether you set the threshold by cost asymmetry and lean 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, picks classification vs survival deliberately, and is judged on retention uplift, not accuracy.