Many years ago, I terribly overfit a model which caused losses of a lot of shekels (a LOT). It’s not that I wasn’t aware of the potential overfitting. I was. Among other things, I used several bootstrapping simulations. It turns out that I applied the bootstrapping in a wrong way. My particular problem was that I “forgot” about confounding parameters and that I “forgot” that peeping into the future is a bad thing.

Anyhow, Yanir Seroussi, my coworker data scientist, gave a very good talk on bootstrapping.

*Bootstrapping the right way* is a talk I gave earlier this year at the YOW! Data conference in Sydney. You can now watch the video of the talk and have a look through the slides. The content of the talk is similar to a post I published on bootstrapping pitfalls, with some additional simulations.

The main takeaways shared in the talk are:

- Don’t compare single-sample confidence intervals by eye
- Use enough resamples (15K?)
- Use a solid bootstrapping package (e.g., Python ARCH)
- Use the right bootstrap for the job
- Consider going parametric Bayesian
- Test all the things

Testing all the things typically requires writing code, which I did for the talk. You can browse through it in this notebook. The most interesting findings from my tests are summarised by the following figure.

The figure shows how the accuracy of confidence interval estimation varies by algorithm, sample size…

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