Chapter 04.06: Resampling 1
Different resampling techniques help to assess the performance of a learner while avoiding potential quirks resulting from a single train-test split. We will introduce cross-validation (with and without stratification), bootstrap and subsampling.
Lecture video
Lecture slides
Code demo
Resampling
You can run the code snippets in the demos on your local machine. The corresponding Rmd version of this demo can be found here. If you want to render the Rmd files to PDF, you need the accompanying style files.
Quiz
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## Which statements are true?
- [x] Cross-validation, bootstrap, and subsampling are resampling techniques.
- [x] Estimating the expected generalization error is a goal of resampling.
- [ ] In bootstrap, each observation serves as a test point exactly once.
- [x] In resampling, the data set is split repeatedly into training and tests sets.
- [ ] Resampling strategies are unbiased.