Deep Forest: Towards An Alternative to Deep Neural Networks Zhi-Hua Zhou and Ji Feng National Key Laboratory 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 contrast to deep neural networks which require great effort in hyper- parameter tuning, gcForest is much easier to train. Actually, even when gcForest is applied to differ- ent data from different domains, excellent perfor- mance can be achieved by almost same settings of hyper-parameters. The training process of gcFor- est is efficient and scalable. In our experiments its training time running on a PC is comparable to that of deep neural networks running with GPU facili- ties, and the efficiency advantage may be more ap- parent because gcForest is naturally apt to parallel implementation. Furthermore, in contrast
