In this paper, we present an efficient equalizer based on random forest for channel equalization in optical fiber communication systems. The results are experimentally demonstrated in a 120 Gb/s, 375 km, dual-polarization 64-quadrature magnitude modulation (QAM) optical fiber communication platform. Based on the optimal parameters, we choose a series of deep learning algorithms for comparison. We find that random forest has the same level of equalization performance as deep neural networks as well as lower computational complexity. Moreover, we propose a two-step classification mechanism. We first divide the constellation points into two regions and then use different random forest equalizers to compensate the points in different regions. Based on this strategy, the system complexity and performance can be further reduced and improved. Furthermore, due to the plurality voting mechanism and two-stage classification strategy, the random forest-based equalizer can be applied to actual optical fiber communication systems.