2021
DOI: 10.18034/ei.v9i2.559
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Significant of Gradient Boosting Algorithm in Data Management System

Abstract: Gradient boosting machines, the learning process successively fits fresh prototypes to offer a more precise approximation of the response parameter. The principle notion associated with this algorithm is that a fresh base-learner construct to be extremely correlated with the “negative gradient of the loss function” related to the entire ensemble. The loss function's usefulness can be random, nonetheless, for a clearer understanding of this subject, if the “error function is the model squared-error loss”, then … Show more

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Cited by 11 publications
(5 citation statements)
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“…One tree is included at a time to the model's current trees. After adding trees, a gradient descent method is employed to reduce the loss [21].…”
Section: Boosting Methodsmentioning
confidence: 99%
“…One tree is included at a time to the model's current trees. After adding trees, a gradient descent method is employed to reduce the loss [21].…”
Section: Boosting Methodsmentioning
confidence: 99%
“…The gradient boosting approach is improved with the development of this supervised ML algorithm, which is based on the ensemble method. By aggregating the predictions of base learners, the XGBoost algorithm [57,58] uses additive approaches to build an effective learning model. In addition to being quick and efficient, the XGBoost classier solves the overflow issue and maximizes the use of computational resources.…”
Section: Extreme Gradient Boost (Xgb)mentioning
confidence: 99%
“…The ensemble method is the foundation for this supervised machine learning algorithm, which enhances the gradient-boosting methodology. Through additive techniques, the XGBoost algorithm [61,62] constructs an efficient learning model by averaging the predictions of base learners. The XGBoost classier solves the overflow problem and maximizes the use of computational resources and is fast and efficient.…”
Section: Extreme Gradient Boost (Xgb)mentioning
confidence: 99%