Open AccessPENERAPAN GRADIENT BOOSTING DENGAN HYPEROPT UNTUK MEMPREDIKSI KEBERHASILAN TELEMARKETING BANK
Author(s) -
Silvia Elsa Suryana,
Budi Warsito,
Suparti Suparti
Publication year - 2021
Publication title -
jurnal gaussian : jurnal statistika undip
Language(s) - English
Resource type - Journals
ISSN - 2339-2541
DOI - 10.14710/j.gauss.v10i4.31335
Subject(s) - hyperparameter , boosting (machine learning) , gradient boosting , decision tree , computer science , machine learning , artificial intelligence , random forest , hyperparameter optimization , python (programming language) , support vector machine , operating system
Telemarketing is another form of marketing which is conducted via telephone. Bank can use telemarketing to offer its products such as term deposit. One of the most important strategy to the success of telemarketing is opting the potential customer to create effective telemarketing. Predicting the success of telemarketing can use machine learning. Gradient boosting is machine learning method with advanced decision tree. Gardient boosting involves many classification trees which are continually upgraded from previous tree. The optimal classification result cannot be separated from the role of the optimal hyperparameter. Hyperopt is Python library that can be used to tune hyperparameter effectively because it uses Bayesian optimization. Hyperopt uses hyperparameter prior distribution to find optimal hyperparameter. Data in this study including 20 independent variables and binary dependent variable which has ‘yes’ and ‘no’ classes. The study showed that gradient boosting reached classification accuracy up to 90,39%, precision 94,91%, and AUC 0,939. These values describe gradient boosting method is able to predict both classes ‘yes’ and ‘no’ relatively accurate.