Ensemble methods involving neural networks have gained prominence as a robust approach for quantifying prediction uncertainty in ML. This technique entails the creation of multiple distinct neural networks, each sharing the same architecture but trained independently using the same dataset. The ensemble yields a collection of predictions, which, under the assumption of normality, can be represented by a Gaussian distribution characterized by its mean and standard deviation.