Random forest has gained extensive attention since its publication in 2001. Random forest can handle both regression and classification with minimum assumptions (no need for normality, homogeneity of variance, and independence between explanatory variables), so that its applications has dramatically increased. Someone even use it as an omnipotent tool for all analysis. In fact, random forest is a specific algorithm with clear characteristics. It is an ensemble method by constructing a number of decision trees, which intends to use local optimization to fit data. When the data have strong partial effect, random forest usually does not fit well. I compared the performance of random forest with multiple regression models, generalized additive models, and artificial neural network using the occurrence data of Cicadidea species. The results showed, although the prediction of random forest looked fragmented, it outperformed the other three models. Random forest also performed better than linear discriminant analysis for classifications. Random forest has its strength and weakness. I suggestion to use multiple models for data analysis rather than one “powerful” model.