What is the difference between cross-validation and bootstrapping?

What is the difference between cross-validation and bootstrapping?

Cross-validation is a technique used to assess how the results of a statistical analysis will generalize to an independent dataset. Bootstrapping, on the other hand, is a resampling method used to estimate the distribution of a statistic by repeatedly sampling with replacement from the data.

Can you explain how each method works?

Sure! In cross-validation, the dataset is divided into 'k' subsets, and the model is trained on 'k-1' subsets while being tested on the remaining subset. This process is repeated 'k' times. Bootstrapping involves randomly sampling data points with replacement to create multiple datasets, allowing for the estimation of the sampling distribution of a statistic.

When should I use one method over the other?

Use cross-validation when you want to evaluate the performance of a model and ensure it generalizes well. Bootstrapping is useful when you want to estimate the uncertainty of a statistic or when your dataset is small.

Are there any limitations to these methods?

Yes, cross-validation can be computationally expensive, especially with large datasets. Bootstrapping may lead to overfitting if the sample size is too small, as it relies heavily on the original dataset.