« Classificateur à renforcement de gradient » : différence entre les versions
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''' | '''Classificateur à renforcement de gradient'''<small>masculin</small> | ||
== Anglais == | == Anglais == | ||
''' | '''Gradient boosting classifier''' | ||
Gradient boosting classifiers are a group of machine learning algorithms that combine many weak learning models together to create a strong predictive model. Decision trees are usually used when doing gradient boosting. Gradient boosting models are becoming popular because of their effectiveness at classifying complex datasets, and have recently been used to win many Kaggle data science competitions. | |||
The Python machine learning library, Scikit-Learn, supports different implementations of gradient boosting classifiers, including XGBoost. | |||
<small> | |||
[https://stackabuse.com/gradient-boosting-classifiers-in-python-with-scikit-learn/ Source : stackabuse.com ] | |||
[[Catégorie:Vocabulaire]] | |||
[[Catégorie:Coulombe]] | |||
[[Category:Apprentissage automatique]] | |||
Version du 3 janvier 2021 à 23:50
en construction
Définition
Français
Classificateur à renforcement de gradientmasculin
Anglais
Gradient boosting classifier
Gradient boosting classifiers are a group of machine learning algorithms that combine many weak learning models together to create a strong predictive model. Decision trees are usually used when doing gradient boosting. Gradient boosting models are becoming popular because of their effectiveness at classifying complex datasets, and have recently been used to win many Kaggle data science competitions.
The Python machine learning library, Scikit-Learn, supports different implementations of gradient boosting classifiers, including XGBoost.
Contributeurs: Claude Coulombe, wiki, Sihem Kouache
