Estimating Uncertainty in Machine Learning Models — Part 1

The text discusses the importance of estimating uncertainty in predictive models, particularly highlighting two types of uncertainty: aleatoric, which is inherent and irreducible in the process, and epistemic, which arises from inadequate knowledge and is reducible with more data or better models. Through a hypothetical example involving drones and a real-world scenario of a bakery estimating cake sales, the text illustrates the application of linear regression models and bootstrapping techniques to quantify uncertainty. By utilizing bootstrap sampling, the article shows how to calculate confidence intervals for model parameters and prediction intervals for outputs. It emphasizes the limitations of these methods in handling complex models and the challenges when normality assumptions are not met, setting the stage for further exploration of uncertainty quantification in more advanced models in a subsequent discussion.

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