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[1J3-OS-10-04] Quantitative Analysis of Improving Classification Accuracy and Computational Cost for Hyperparameter Optimization
Keywords:Hyperparameter Optimization , Automated Machine Learning(AutoML), ROI(Return on Investment)
Hyperparameter optimization(HPO), a research part of AutoML, strongly impact performance of deep learning algorithms.
However, in order to have exploded computational costs of machine learning, when system optimized hyperparameter introduce to the real world, we need to discuss cost-effectiveness about HPO.
In this paper, we show improvement of classification accuracy and computational cost for HPO of image classification problem.
Also, we estimate economic effects from improvement of classification accuracy and propose quantitative evaluation method of cost-effectiveness of HPO in image classification problem.
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