Abstract
This research aims to figure out the best hyperparameter-selection acquisition functions on the specific neural network model. Three Bayesian optimization approaches are discussed in the paper, including Bayesian and Local Optimization Sample-wise Switching Optimization Method (BLOSSOM), Predictive Entropy Search (PES), and Expected Improvement (EI). The data set acquired from the UCI Machine Learning Repository trained in this research, is about the default of credit card clients data set collecting in Taiwan. By comparing the predictive accuracy result of the estimated probability of default between different neural network models with different Bayesian optimization, this paper will present each acquisition function's performance and select the best one to tune the neural network model.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
