6.22 Resampling methods (1)

• Resampling methods (James et al. 2013, Ch. 5)
  • Repeatedly draw samples from training set and refit model of interest on each sample to obtain additional information about the fitted model, e.g., the variability of a logistic regression fit
• Aim: Obtain information that would not be available from fitting model only once using the original training sample
  
• Model assessment: Process of evaluating a model’s performance
• Model selection: Process of selecting the proper level of flexibility for a model
• Common resampling methods: Cross-validation (CV) and bootstrap
  • Cross-validation can be used to estimate the test error associated with a given statistical learning method in order to evaluate its performance, or to select the appropriate level of flexibility
  • Bootstrap most commonly used in model selection to provide a measure of accuracy of a parameter estimate or of a given statistical learning method
• Here we discuss cross-validation!

References

James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. 2013. An Introduction to Statistical Learning: With Applications in R. Springer Texts in Statistics. Springer.