---
title: "Utilizing target permutation to prevent overfitting."
url: https://daily.dev/posts/utilizing-target-permutation-to-prevent-overfitting--60nw7ihy8
source_url: https://medium.com/@uriitai/utilizing-target-permutation-to-prevent-overfitting-44a141bf70e0
type: article
source: "Medium"
published: 2024-01-09T12:11:36.858Z
updated: 2024-01-09T12:11:35.863Z
tags: ["ai", "data-science", "machine-learning", "feature-engineering", "overfitting"]
reading_time: 4
upvotes: 0
comments: 0
language: en
---

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# Utilizing target permutation to prevent overfitting.

**[Medium](https://daily.dev/sources/medium_js)** · 4 min read · 0 upvotes · 0 comments

## Summary

The author describes a method to assess whether the results of a model are random or statistically significant by permuting the target vector in the training set and evaluating the results on the original target in the test set. The results showed that the non-permuted target vector outperformed most randomly permuted vectors, but there were instances where the model trained better with a randomly permuted vector.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://medium.com/@uriitai/utilizing-target-permutation-to-prevent-overfitting-44a141bf70e0>

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---

Tags: [#ai](https://daily.dev/tags/ai), [#data-science](https://daily.dev/tags/data-science), [#machine-learning](https://daily.dev/tags/machine-learning), [#feature-engineering](https://daily.dev/tags/feature-engineering), [#overfitting](https://daily.dev/tags/overfitting)

[View this post on daily.dev](https://daily.dev/posts/utilizing-target-permutation-to-prevent-overfitting--60nw7ihy8)
