precision recall

A common ML-coding check that verifies you actually understand the metrics you quote. The signal is getting TP/FP/FN/TN right and knowing macro vs micro averaging for multiclass, not reaching for sklearn.

Building a PR/ROC curve from scratch proves you understand thresholds and the precision/recall tradeoff. The real signal is sweeping the threshold in one sorted pass and integrating the area. Here is the implementation.

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.

Defaulting to 0.5 is leaving money or safety on the table. The right cutoff comes from the cost of each error and the operating constraint, not the model. Here is how to set it deliberately.

Moderation is a multi-stage classification problem with brutal tradeoffs: false negatives cause real harm, false positives censor legitimate users, and the adversary adapts. The signal is the tiered pipeline, per-severity precision/recall calibration, and human-in-the-loop. Here is the design.