Random forest algorithm in big data environment

Random forest method is one of the most widely applied classification algorithms at present. From the actual big data scene and requirements, the application of random forest method in the big data environment to conduct in-depth study. Due to the big data needs to process a huge number of features at the same time, and the data pattern changes constantly over time, the accuracy of a random forest algorithm without self-renewal and adaptive algorithm will gradually reduce over time. Aiming at this problem, analysis on the characteristics of random forest method, presents how to realize the self-adaptation ability with random forest method in similar situations, and verified the feasibility of the new method of using the actual data, and analysis and discussion of how to further research and improve the random forest method in big data environment.