Author:
Valdez-Valenzuela Eric,Kuri-Morales Angel,Gomez-Adorno Helena
Publisher
Springer Nature Switzerland
Reference19 articles.
1. Kuhn, M., Johnson, K.: Feature Engineering and Selection: A Practical Approach for Predictive Models. Chapman and Hall/CRC, Boca Raton (2019)
2. Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
3. Ke, G., et al.: Lightgbm: a highly efficient gradient boosting decision tree. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
4. Chen, T., Guestrin, C.: Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016)
5. Hancock, J.T., Khoshgoftaar, T.M.: Survey on categorical data for neural networks. J. Big Data 7(1), 1–41 (2020)