Machine Learning & Data Science Supervised Learning Bootstrapping

Bootstrapping is a statistical resampling technique used to produce a number of representative datasets from a single dataset using sampling with replacement. It is particularly useful when the underlying distribution of the dataset is unknown, or when analytical methods of estimating distribution parameters is not possible.

Applications

Confidence Intervals

Bootstrapping can provide confidence intervals for a statistic without relying on strong parametric assumptions (e.g., normality of data).

Standard Error Estimation

The variability of the bootstrap samples can be used to estimate the standard error of a statistic.

Model Validation

In machine learning, bootstrap sampling is used in bagging algorithms like Random Forest to train models on different subsets of the data.

Hypothesis Testing

Bootstrapping allows for hypothesis testing by creating a null distribution from the resampled data.