precision recall

A common ML-coding check that confirms 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 shows you understand thresholds and the precision/recall tradeoff. What interviewers really want is sweeping the threshold in one sorted pass and integrating the area. The implementation follows.

Writing the regexes takes ten minutes. The half of the question that separates candidates is the evaluation: a detector that blocks 'ignore the noise in the data' has shipped a bug to every analyst using your product. Here is the layered detector and the harness that proves it works.

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.

Defaulting to 0.5 leaves 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 harsh tradeoffs: false negatives cause real harm, false positives silence legitimate users, and the adversary keeps adapting. What interviewers reward: the tiered pipeline, per-severity precision/recall calibration, and human-in-the-loop. This lays out the design.