False Negative Prediction

False negative predictions, where a model incorrectly identifies a true positive as negative, represent a significant challenge across diverse machine learning applications. Current research focuses on mitigating these errors through improved data quality (e.g., identifying and correcting noisy negative examples in training datasets), enhanced model architectures (e.g., incorporating uncertainty estimation and counterfactual explanations), and novel loss functions that penalize implausible predictions. Addressing false negatives is crucial for improving the reliability and trustworthiness of machine learning models, particularly in high-stakes domains like medical diagnosis and financial risk assessment, where accurate predictions are paramount.