Question

A data science team must build a text-classification model to route incoming support tickets into 12 categories. They have only 400 labeled tickets total but millions of unlabeled historical tickets. Management wants the highest accuracy possible while minimizing new labeling effort. Which modeling approach best fits this situation?

Accepted Answer

Fine-tune a pre-trained language model (transfer learning) on the 400 labeled tickets.

Answer

Train a linear classifier from scratch on the 400 labeled tickets using bag-of-words features.

Answer

Use k-means clustering on all tickets and label each cluster manually as one of the 12 categories.

Answer

Discard the labeled data and rely solely on rule-based keyword matching for each category.