High Confidence Prediction

High-confidence prediction focuses on improving the reliability and trustworthiness of machine learning models, particularly in addressing the issue of overconfidence where models assign high certainty to incorrect predictions. Current research emphasizes developing methods to calibrate model confidence, often employing techniques like feature selection, self-reflective rationales, and uncertainty quantification using dropout or ensemble methods within various architectures including neural networks and graph convolutional networks. This work is crucial for enhancing the dependability of AI systems across diverse applications, from autonomous driving and medical diagnosis to financial forecasting and natural language processing, where accurate confidence estimates are essential for safe and effective deployment.