When optimism hurts

machine-learning

Data leakage in machine learning

Published

February 26, 2024

A question very close to my heart is whether all the “test scores” we report in our machine learning studies actually mean anything.

While there are a lot of choices and potential problems just in the fact of deciding to perform a quantiative analysis and the metrics we choose [see machine learning that matters from Kiri Wagstaff as well as Goodhart’s law and the shortcut rule] a very common pitfall is that we do not have independent test data.

see the hierachy of limitations of machine learning from Momin M. Malik on this

In the statistical learning community, this has been known as optimism and is conventionally used to show how the training error is not a good estimate of generalization performance.

-> we can derive optimism and then think of the test dataset as a mix of 100% train, 0% train, or a mix

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title: 'When optimism hurts'
description: <i>Data leakage in machine learning</i>
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categories:
  - machine-learning
date: "2/26/2024"
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A question very close to my heart is whether all the "test scores" we report in our machine learning studies actually mean anything.

While there are a lot of choices and potential problems just in the fact of deciding to perform a quantiative analysis [see [the hierachy of limitations of machine learning from Momin M. Malik on this](https://arxiv.org/abs/2002.05193)]{.aside} and the metrics we choose [see [machine learning that matters from Kiri Wagstaff](https://arxiv.org/abs/1206.4656v1) as well as Goodhart's law and the shortcut rule] a very common pitfall is that we do not have independent test data.

In the statistical learning community, this has been known as _optimism_ and is conventionally used to show how the training error is not a good estimate of generalization performance. 

-> we can derive optimism and then think of the test dataset as a mix of 100% train, 0% train, or a mix