False Positive Rate

False positive rate (FPR), the proportion of incorrectly identified positive instances, is a critical metric across diverse fields, driving research focused on minimizing its occurrence and accurately estimating its value. Current research emphasizes developing and evaluating methods to reduce FPR in various applications, including anomaly detection (using techniques like student-teacher networks and Bayesian inference), machine learning model trustworthiness assessment, and facial recognition (where image quality significantly impacts FPR). Understanding and controlling FPR is crucial for ensuring reliable system performance and mitigating potential biases or risks in areas such as healthcare, autonomous driving, and security systems.