What does bootstrapping help estimate in model evaluation?

Bootstrapping is a resampling technique used in statistics that helps to estimate the distribution of a statistic by repeatedly sampling from a dataset, with replacement. This method allows for the generation of multiple simulated samples from the original data, which can then be used to assess variability and derive confidence intervals for different statistics, such as the mean or median.

In model evaluation, bootstrapping is particularly useful because it allows practitioners to understand the potential variability of model metrics (like accuracy, precision, and recall) without needing to rely on a single split of the data into training and testing subsets. By examining how the statistic behaves across multiple bootstrapped samples, one can obtain a more robust estimate of its distribution, which can inform decisions about the reliability of model predictions and how they might generalize to unseen data.

This practical application in evaluating model performance underlines the importance of bootstrapping as a technique for assessing statistical properties and providing insight into the robustness of model estimates.