Uncertainty Quantification in Machine Learning using an Ensemble Approach with Gaussian Process Regression

Machine learning has witnessed widespread adoption across various domains, bringing about transformative changes in decision-making, trend prediction, task automation, and personalized experiences. Despite the remarkable predictive capabilities of machine learning models, the associated uncertainty in their predictions remains a critical concern. Uncertainty estimation plays a pivotal role in ensuring robust decision-making, going beyond mere outcome prediction to quantify the model's confidence and potential error. This paper first presents a review of existing uncertainty quantification techniques in machine learning, including Monte Carlo dropout and ensemble methods, highlighting their advantages in addressing uncertainty as well as their limitations. Then, it presents an efficient and fast novel technique for uncertainty quantification using a combination of the ensemble technique and Gaussian process regression providing an accurate estimation of uncertainty bounds. Due to its accuracy and efficiency, the proposed method is well-suited for real-time applications involving scalar or time series data. The advantages of the proposed method are demonstrated using a mathematical example and a vehicle dynamics example.