Bootstrap#
Resampling with replacement to estimate the variability of a statistic.
Important
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What it is#
The bootstrap is a resampling method that draws new samples with replacement from the observed data — each resample the same size as the original, with some points repeated and others omitted. From many such resamples it estimates a statistic’s sampling distribution.
What it’s for#
By recomputing a statistic (a mean, an AUC) across hundreds or thousands of bootstrap resamples, you get its standard error and confidence intervals without assuming a formula or a distribution. That makes it a flexible, non-parametric way to quantify uncertainty.
Where it appears#
The same idea powers bagging (bootstrap aggregating) and random forests, which train each model on a different bootstrap sample to reduce variance. Its main cost is compute — many refits — and it can struggle with very small samples or extreme statistics.
Theme: Model Evaluation & Uncertainty · All terminology
Hint
Mind map — connected ideas
Bootstrap Confidence Intervals (CIs) · Standard Error (SE) · Upsampling · Downsampling · Decision Trees · Confidence Intervals (CIs)
Hint
More in Model Evaluation & Uncertainty
Average Absolute Error (AAE) · Baseline Heuristics · Bootstrap Confidence Intervals (CIs) · Coverage · Cramér’s V · DeLong’s Test · KS Statistic (Kolmogorov–Smirnov Statistic) · Likelihood Ratio (LR) · Mann–Whitney U Test (also called the Wilcoxon rank-sum test) · MASE (Mean Absolute Scaled Error) · Mean Absolute Error (MAE) · Mean Absolute Percentage Error (MAPE) · Mean Squared Error (MSE) · Relative accuracy
See also
Source article Adapted (context, re-expressed) in our own words from: Bootstrap (insightful-data-lab.com).