Internet companies are facing the need of handling large scale machine learning applications in a daily basis, and distributed system which can handle extra-large scale tasks is needed. Deep forest is a recently proposed deep learning framework which uses tree ensembles as its building blocks and it has achieved highly competitive results on various domains of tasks. However, it has not been tested on extremely large scale tasks. In this work, based on our parameter server system and platform of artificial intelligence, we developed the distributed version of deep forest with an easy-to-use GUI. To the best of our knowledge, this is the first implementation of distributed deep forest. To meet the need of real-world tasks, many improvements are introduced to the original deep forest model. We tested the deep forest model on an extra-large scale task, i.e., automatic detection of cash-out fraud, with more than 100 millions of training samples. Experimental results showed that the deep forest model has the best performance according to the evaluation metrics from different perspectives even with very little effort for parameter tuning. This model can block fraud transactions in a large amount of money \footnote{detail is business confidential} each day. Even compared with the best deployed model, deep forest model can additionally bring into a significant decrease of economic loss.

本研究基于原始deep forest模型，引入MART基础学习者，成本方法，MART特征选择和不同评价指标等模型改进，开发出可用于极大规模任务的分布式deep forest模型，检测到超过1亿个训练样本的现金提款欺诈，实验结果表明该模型具有最佳性能，可以阻止每天大量的欺诈交易和显著降低经济损失。

分布式深度森林及其在自动检测提现欺诈中的应用