Showing posts from October, 2020

Epistemic and aleatoric uncertainty in machine learning

Error in a machine learning prediction is a combination of epistemic and aleatoric uncertainty. Understanding the two is essential for model improvement and explaining the model performance. For example, getting more data can decrease only the epistemic uncertainty but not the aleatoric uncertainty, meaning you are tackling only one source of the error.