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# Understanding the Causes of Overfitting: A Mathematical Perspective

**[Medium](https://daily.dev/sources/medium_js)** · 6 min read · 2 upvotes · 0 comments

## Summary

This post explores the causes of overfitting in machine learning from a mathematical perspective. It discusses the impact of model complexity, the bias-variance tradeoff, and data dimensionality on overfitting. Key mathematical concepts, such as VC dimension and regularization, are also explained. The post concludes by emphasizing the importance of balancing model accuracy and simplicity to avoid overfitting.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://medium.com/aimonks/understanding-the-causes-of-overfitting-a-mathematical-perspective-09af234e9ce4>

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

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