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- Gradient_boosting abstract "Gradient boosting is a machine learning technique for regression problems, which produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees. It builds the model in a stage-wise fashion like other boosting methods do, and it generalizes them by allowing optimization of an arbitrary differentiable loss function. The gradient boosting method can also be used for classification problems by reducing them to regression with a suitable loss function.The method was invented by Jerome H. Friedman in 1999 and was published in a series of two papers, the first of which introduced the method, and the second one described an important tweak to the algorithm, which improves its accuracy and performance.".
- Gradient_boosting wikiPageID "26649339".
- Gradient_boosting wikiPageRevisionID "603393973".
- Gradient_boosting hasPhotoCollection Gradient_boosting.
- Gradient_boosting subject Category:Decision_trees.
- Gradient_boosting subject Category:Ensemble_learning.
- Gradient_boosting type DecisionTrees.
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- Gradient_boosting comment "Gradient boosting is a machine learning technique for regression problems, which produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees. It builds the model in a stage-wise fashion like other boosting methods do, and it generalizes them by allowing optimization of an arbitrary differentiable loss function.".
- Gradient_boosting label "Gradient boosting".
- Gradient_boosting sameAs m.0bh7mq_.
- Gradient_boosting sameAs Q5591907.
- Gradient_boosting sameAs Q5591907.
- Gradient_boosting sameAs Gradient_boosting.
- Gradient_boosting wasDerivedFrom Gradient_boosting?oldid=603393973.
- Gradient_boosting isPrimaryTopicOf Gradient_boosting.