Enhancing decision confidence in AI using Monte Carlo dropout for Raman spectra classification

Different from routine prediction based on mere probabilities, we believe this uncertainty-guided prediction is more effective to ensure a high prediction rate rather than the prediction on the entire dataset. By guiding the decision-making of a model on higher-confidence subsets, our methodology can enhance the accuracy of classification in critical areas like disease diagnosis and safety monitoring. This targeted approach is to advance microbial identification and produces more trustworthy predictions.