According to AAISM technical content, supervised learning models reduce false positives by learning from historical labeled data that distinguishes between legitimate activity and actual threats. This training enables the model to recognize patterns and improve its discrimination ability over time. Grouping patterns (A) describes clustering, an unsupervised method. Real-time feature engineering (B) and generating new labeled data (D) are advanced techniques but not the fundamental supervised learning approach. The essence of supervised learning is leveraging labeled data to minimize misclassification, including false positives.