Unreliable Prediction

Unreliable predictions in machine learning models, stemming from various sources like data imbalance, noisy inputs, and model limitations, are a significant concern across diverse applications. Current research focuses on improving prediction reliability through techniques such as uncertainty quantification (using methods like conformal prediction and evidential deep learning), developing more robust model architectures (including Bayesian approaches and ensemble methods), and refining training strategies (e.g., incorporating self-supervised learning and addressing inconsistencies in predictions). Addressing this challenge is crucial for building trustworthy AI systems, particularly in high-stakes domains like healthcare and finance, where inaccurate predictions can have serious consequences.