A Comparative Analysis of Hyperopt as Against Other Approaches for Hyper-Parameter Optimization of XGBoost

Author:

Putatunda Sayan1,Rama Kiran1

Affiliation:

1. VMware Software India pvt. Ltd., Kalyani Vista, JP Nagar, Bangalore, India

Publisher

ACM Press

Reference29 articles.

1. J. Bergstra, R. Bardenet, Y. Bengio, and B. Kegl, "Algorithms for Hyper-Parameter Optimization," NIPS, pp. 1--9, 2011.

2. J. Snoek, H. Larochelle, and R. P. Adams, "Practical bayesian optimization of machine learning algorithms," in Proc. of NIPS, 2012.

3. J. Bergstra and Y. Bengio, "Random Search for Hyper-Parameter Optimization," J. Mach. Learn. Res., vol. 13, pp. 281--305, 2012.

4. T. Chen and C. Guestrin, "XGBoost: A scalable tree boosting system," in Proceedings of the 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2016, pp. 785--794.

5. R. Schaer, H. Muller, and A. Depeursinge, "Optimized Distributed Hyperparameter Search and Simulation for Lung Texture Classification in CT Using Hadoop," J. Imaging, vol. 2, no. 19, pp. 1--20, 2016.

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