Bootstrap methods are resampling techniques that provide measures of accuracy like confidence intervals and standard errors without relying on parametric assumptions. These techniques are essential in statistical inference when the underlying distribution is unknown or complex.
Cross-Validation is a critical resampling procedure utilized in evaluating machine learning models to ensure accuracy, reliability, and performance.
Resampling involves drawing repeated samples from the observed data, an essential technique in statistics used for estimating the precision of sample statistics by random sampling.
