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Bootstrap aggregating, also called bagging (from bootstrap aggregating), is a machine learning ensemble meta-algorithm designed to improve the stability and accuracy of machine learning algorithms used in statistical classification and regression. It also reduces variance and helps to avoid overfitting. Although it is usually applied to decision tree methods, it can be used with any type of method. Bagging is a special case of the model averaging approach. | Bootstrap aggregating, also called bagging (from bootstrap aggregating), is a machine learning ensemble meta-algorithm designed to improve the stability and accuracy of machine learning algorithms used in statistical classification and regression. It also reduces variance and helps to avoid overfitting. Although it is usually applied to decision tree methods, it can be used with any type of method. Bagging is a special case of the model averaging approach. | ||
==Sources== | |||
[XXXXXXXXXX Source : Source : Wikipedia ] | [XXXXXXXXXX Source : Source : Wikipedia ] | ||
[https://en.wikipedia.org/wiki/Outline_of_machine_learning#Machine_learning_algorithms Source : Wikipedia Machine learning algorithms ] | [https://en.wikipedia.org/wiki/Outline_of_machine_learning#Machine_learning_algorithms Source : Wikipedia Machine learning algorithms ] | ||
Dernière version du 29 janvier 2024 à 11:19
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Définition
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Français
XXXXXXXXX
Anglais
Bootstrap aggregating
Bootstrap aggregating, also called bagging (from bootstrap aggregating), is a machine learning ensemble meta-algorithm designed to improve the stability and accuracy of machine learning algorithms used in statistical classification and regression. It also reduces variance and helps to avoid overfitting. Although it is usually applied to decision tree methods, it can be used with any type of method. Bagging is a special case of the model averaging approach.
Sources
[XXXXXXXXXX Source : Source : Wikipedia ]
Contributeurs: wiki
