Deep Forest: Towards an Alternative to Deep Neural Networks∗ Zhi-Hua Zhou and Ji Feng National Key Lab for Novel Software Technology, Nanjing University, Nanjing 210023, China {zhouzh, fengj}@lamda.nju.edu.cn Abstract In this paper, we propose gcForest, a decision tree ensemble approach with performance highly com- petitive to deep neural networks in a broad range of tasks. In contrast to deep neural networks which re- quire great effort in hyper-parameter tuning, gcFor- est is much easier to train; even when it is applied to different data across different domains in our ex- periments, excellent performance can be achieved by almost same settings of hyper-parameters. The training process of gcForest is efficient, and users can control training cost according to computa- tional resource available. The efficiency may be further enhanced because gcForest is naturally apt to parallel implementation. Furthermore, in con- trast to deep neural networks which require large-
