What is the difference between bootstrapping and cross-validation?

What is the difference between bootstrapping and cross-validation?

Bootstrapping is a resampling technique used to estimate the distribution of a statistic, while cross-validation is a method for assessing how the results of a statistical analysis will generalize to an independent dataset.

Can you explain how bootstrapping works?

Sure! Bootstrapping involves repeatedly sampling from a dataset with replacement to create 'bootstrap' samples. These samples are then used to estimate the variability of a statistic.

And how does cross-validation help in model evaluation?

Cross-validation helps by dividing the dataset into training and testing subsets. It trains the model on one subset and tests it on another, providing a better estimate of model performance.

When should I use bootstrapping instead of cross-validation?

Use bootstrapping when you want to estimate the uncertainty of a statistic, and cross-validation when you need to evaluate model performance and avoid overfitting.