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Use the Same Cross-Plan Between Steps

Students have asked me if it is better to use the same cross-validation plan in each step of an analysis or to use different ones. Our answer is: unless you are coordinating the many plans in some way (such as 2-way independence or some sort of combinatorial design) it is generally better to use one plan. That way minor information leaks at each stage explore less of the output variations, and don’t combine into worse leaks.

I am now sharing a note that works all of the above as specific examples: “Multiple Split Cross-Validation Data Leak” (a follow-up to our larger article “Cross-Methods are a Leak/Variance Trade-Off”).

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Cross-Methods are a Leak/Variance Trade-Off

We have a new Win Vector data science article to share:

Cross-Methods are a Leak/Variance Trade-Off

John Mount (Win Vector LLC), Nina Zumel (Win Vector LLC)

March 10, 2020

We work some exciting examples of when cross-methods (cross validation, and also cross-frames) work, and when they do not work.

Abstract

Cross-methods such as cross-validation, and cross-prediction are effective tools for many machine learning, statisitics, and data science related applications. They are useful for parameter selection, model selection, impact/target encoding of high cardinality variables, stacking models, and super learning. They are more statistically efficient than partitioning training data into calibration/training/holdout sets, but do not satisfy the full exchangeability conditions that full hold-out methods have. This introduces some additional statistical trade-offs when using cross-methods, beyond the obvious increases in computational cost.

Specifically, cross-methods can introduce an information leak into the modeling process. This information leak will be the subject of this post.

The entire article is a JupyterLab notebook, and can be found here. Please check it out, and share it with your favorite statisticians, machine learning researchers, and data scientists.

Posted on Categories Exciting Techniques, Practical Data Science, Pragmatic Data Science, Pragmatic Machine Learning, Statistics, TutorialsTags , , , , , , 1 Comment on PyData Los Angeles 2019 talk: Preparing Messy Real World Data for Supervised Machine Learning

PyData Los Angeles 2019 talk: Preparing Messy Real World Data for Supervised Machine Learning

Video of our PyData Los Angeles 2019 talk Preparing Messy Real World Data for Supervised Machine Learning is now available. In this talk describe how to use vtreat, a package available in R and in Python, to correctly re-code real world data for supervised machine learning tasks.

Please check it out.

(Slides are also here.)