OPTIMASI HYPERPARAMETER PADA GRADIENT BOOSTED TREES MENGGUNAKAN BAYESIAN OPTIMIZATION

RACHMATULLAH, ARIEF and Saputra, Danny Matthew and Primartha, Rifkie (2019) OPTIMASI HYPERPARAMETER PADA GRADIENT BOOSTED TREES MENGGUNAKAN BAYESIAN OPTIMIZATION. Undergraduate thesis, Sriwijaya University.

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Abstract

Data mining is a technique to transform a collection of data into a knowledge. One of the factors that affect the accuracy of these data mining methods is hyperparameters which must be determined before training proses started. The data mining method that is very affected by hyperparameters is Gradient Boosted Trees. With a wrong hyperparameter configuration, the Gradient Boosted Tree model will be overfitting. One way that can be used to prevent this is to optimize Gradient Boosted Trees hyperparameters with hyperparameter optimization techniques and one of the popular hyperparameter optimization methods is Bayesian Optimization. Therefore, this study utilizes Bayesian Optimization to optimize Gradient Boosted Trees hyperparameter.

Item Type: Thesis (Undergraduate)
Uncontrolled Keywords: Data mining, Hyperparameter, optimization, gradient boosted trees, bayesian optimization
Subjects: Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions: 09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User: Users 3896 not found.
Date Deposited: 06 Jan 2020 07:02
Last Modified: 06 Jan 2020 07:02
URI: http://repository.unsri.ac.id/id/eprint/23081

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