Questions tagged [boosting]

For questions related to the concept of boosting in machine learning, which is a collection of ensemble learning methods.

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Multiclass Ensemble Methods with weak classifiers under 50%

Normally, when using an ensemble method, such as baggin or boosting, in binary classification, there is a reqauirment that each weak classifier have accuracy better than 50%. In the multiclass ...
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What ensemble methods are used in the state-of-the-art models?

What ensemble methods are used in the state-of-the-art models? When I surveyed the state-of-the-art methods of classification and detection, e.g. ImageNet, COCO, etc., I noticed that are few or even ...
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Why don't ensembling, bagging and boosting help to improve accuracy of Naive bayes classifier?

You might think to apply some classifier combination techniques like ensembling, bagging and boosting but these methods would not help. Actually, “ensembling, boosting, bagging” won’t help since their ...
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How do weak learners become strong in boosting?

Boosting refers to a family of algorithms which converts weak learners to strong learners. How does it happen?
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Tabular Datasets where Deep neural networks outperforms XGBoost

Are there (complex) tabular datasets where deep neural networks (e.g. more than 3 layers) outperform traditional methods such as XGBoost by a large margin? I'd prefer tabular datasets rather than ...
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Why would the application of boosting prevent underfitting?

"Why would the application of boosting prevent underfitting?" I read in some paper that applying boosting would prevent you from underfitting. Why is that? Source: https://www.cs.cornell.edu/...
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