1. Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M., 2019. Optuna: A next-generation hyperparameter optimization framework, in: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pp. 2623–2631.
2. Alteryx, 2021. EvalML: AutoML library which builds, optimizes, and evaluates machine learning pipelines using domain-specific objective functions. URL: https://github.com/alteryx/evalml. version 0.23.0.
3. Bergstra, J., Yamins, D., Cox, D., 2013. Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures, in: International conference on machine learning, PMLR. pp. 115–123.
4. Erickson, N., Mueller, J., Shirkov, A., Zhang, H., Larroy, P., Li, M., Smola, A., 2020. Autogluon-tabular: Robust and accurate automl for structured data. arXiv preprint arXiv:2003.06505 .
5. Auto-sklearn: efficient and robust automated machine learning;Feurer,2019