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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2312.02573 |
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| _version_ | 1866915141734367232 |
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| author | Gao, Junjie Zheng, Xiangyu Wang, DongDong Huang, Zhixiang Zheng, Bangqi Yang, Kai |
| author_facet | Gao, Junjie Zheng, Xiangyu Wang, DongDong Huang, Zhixiang Zheng, Bangqi Yang, Kai |
| contents | Uplift modeling comprises a collection of machine learning techniques designed for managers to predict the incremental impact of specific actions on customer outcomes. However, accurately estimating this incremental impact poses significant challenges due to the necessity of determining the difference between two mutually exclusive outcomes for each individual. In our study, we introduce two novel modifications to the established Gradient Boosting Decision Trees (GBDT) technique. These modifications sequentially learn the causal effect, addressing the counterfactual dilemma. Each modification innovates upon the existing technique in terms of the ensemble learning method and the learning objective, respectively. Experiments with large-scale datasets validate the effectiveness of our methods, consistently achieving substantial improvements over baseline models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_02573 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | UTBoost: Gradient Boosted Decision Trees for Uplift Modeling Gao, Junjie Zheng, Xiangyu Wang, DongDong Huang, Zhixiang Zheng, Bangqi Yang, Kai Machine Learning Artificial Intelligence Uplift modeling comprises a collection of machine learning techniques designed for managers to predict the incremental impact of specific actions on customer outcomes. However, accurately estimating this incremental impact poses significant challenges due to the necessity of determining the difference between two mutually exclusive outcomes for each individual. In our study, we introduce two novel modifications to the established Gradient Boosting Decision Trees (GBDT) technique. These modifications sequentially learn the causal effect, addressing the counterfactual dilemma. Each modification innovates upon the existing technique in terms of the ensemble learning method and the learning objective, respectively. Experiments with large-scale datasets validate the effectiveness of our methods, consistently achieving substantial improvements over baseline models. |
| title | UTBoost: Gradient Boosted Decision Trees for Uplift Modeling |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2312.02573 |