mlquantify
Algorithms for Class Distribution Estimation
Quantification is a prominent machine learning task that has received an increasing amount of attention in the last years. The objective is to predict the class distribution of a data sample. This package is a collection of machine learning algorithms for class distribution estimation. This package include algorithms from different paradigms of quantification. These methods are described in the paper: A. Maletzke, W. Hassan, D. dos Reis, and G. Batista. The importance of the test set size in quantification assessment. In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI20, pages 2640–2646, 2020. doi:10.24963/ijcai.2020/366.
- Version0.2.0
- R versionunknown
- LicenseGPL-2
- LicenseGPL-3
- Needs compilation?No
- A. Maletzke, W. Hassan, D. dos Reis, and G. Batista. The importance of the test set size in quantification assessment. In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI20, pages 2640–2646, 2020.
- Last release01/20/2022
Documentation
Team
Andre Maletzke
Everton Cherman
Show author detailsRolesContributorDenis dos Reis
Show author detailsRolesContributorGustavo Batista
Show author detailsRolesThesis advisor
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- Imports3 packages
- Suggests1 package