# margins

Published articles for margins.

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## The Boosting Margin, or Why Boosting Doesn't Overfit

DevFeed: [The Boosting Margin, or Why Boosting Doesn't Overfit](<https://devfeed.tech/articles/the-boosting-margin-or-why-boosting-doesn-t-overfit-40388.md>)

Original publisher: [Read original article](<https://www.jeremykun.com/2015/09/21/the-boosting-margin-or-why-boosting-doesnt-overfit/>)

Published: 2015-09-21T11:33:00Z

Content type: article

Language: en

Sources: [Jeremy Kun](<https://devfeed.tech/sources/jeremy-kun.md>)

Topics: [machine learning overfitting](<https://devfeed.tech/topics/machine-learning-overfitting.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [generalization in machine learning](<https://devfeed.tech/topics/generalization-in-machine-learning.md>), [Occam's razor machine learning](<https://devfeed.tech/topics/occam-s-razor-machine-learning.md>), [Algorithm](<https://devfeed.tech/topics/algorithm.md>)

Tags: [algorithm](<https://devfeed.tech/tags/algorithm.md>), [boosting](<https://devfeed.tech/tags/boosting.md>), [chernoff-bound](<https://devfeed.tech/tags/chernoff-bound.md>), [classficiation](<https://devfeed.tech/tags/classficiation.md>), [complexity](<https://devfeed.tech/tags/complexity.md>), [conditional-probability](<https://devfeed.tech/tags/conditional-probability.md>), [error](<https://devfeed.tech/tags/error.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [margins](<https://devfeed.tech/tags/margins.md>), [mathematics](<https://devfeed.tech/tags/mathematics.md>), [model](<https://devfeed.tech/tags/model.md>), [occam-s-razor](<https://devfeed.tech/tags/occam-s-razor.md>), [overfitting](<https://devfeed.tech/tags/overfitting.md>), [training-data](<https://devfeed.tech/tags/training-data.md>), [vc-dimension](<https://devfeed.tech/tags/vc-dimension.md>)

### AI overview

This article explains why boosting can continue improving generalization after reaching zero training error. It introduces the margin-based theoretical explanation for this behavior and defines the confidence and margin of AdaBoost classifiers.

### Source excerpt

There's a well-understood phenomenon in machine learning called overfitting. The idea is best shown by a graph: overfitting Let me explain. The vertical axis represents the error of a hypothesis. The horizontal axis represents the complexity of the hypothesis. The blue curve represents the error of a machine learning algorithm's output on its training data, and the red curve represents the generalization of that hypothesis to the real world. The overfitting phenomenon is marker in the middle of the graph, before which the training error and generalization error both go down, but after which the training error continues to fall while the generalization error rises.