Uplift modeling has been used effectively in fields such as marketing and
customer retention, to target those customers who are more likely to respond
due to the campaign or treatment. Essentially, it is a machine learning
technique that predicts the gain from performing some action with respect to
not taking it. A popular class of uplift models is the transformation approach
that redefines the target variable with the original treatment indicator. These
transformation approaches only need to train and predict the difference in
outcomes directly. The main drawback of these approaches is that in general it
does not use the information in the treatment indicator beyond the construction
of the transformed outcome and usually is not efficient. In this paper, we
design a novel transformed outcome for the case of the binary target variable
and unlock the full value of the samples with zero outcome. From a practical
perspective, our new approach is flexible and easy to use. Experimental results
on synthetic and real-world datasets obviously show that our new approach
outperforms the traditional one. At present, our new approach has already been
applied to precision marketing in a China nation-wide financial holdings group.

通过重新定义目标变量和解锁具有零结果样本的全部价值，本论文设计了一种新型转化后果，实验结果明显显示出该新方法在合成和实际数据集上优于传统方法，并已应用于中国一家全国性金融控股集团的精准营销。