基于改进网格搜索算法的随机森林参数优化

随机森林是一种有效的集成学习算法，被广泛应用于模式识别中。为了得到更高的预测精度，需要对参数进行优化。提出了一种基于袋外数据估计的分类误差，利用改进的网格搜索算法对随机森林算法中的决策树数量和候选分裂属性数进行参数优化的随机森林算法。仿真结果表明，利用该方法优化得到的参数都能够使随机森林的分类效果得到一定程度的提高。

Parameter optimization method for random forest based on improved grid search algorithm

Random forest is an effective ensemble learning method, which is widely used in pattern recognition. In order to get higher accuracy, it is necessary to optimize the parameter of random forest. Based on generalization error of out-of-bag estimates, this paper proposes a parameter optimization method for a random forest with improved grid search. The parameter of the number of decision trees and candidate splitting attributes is optimized to improve accuracy. The simulation results demonstrates that optimized parameter by the method proposed in this paper makes the classification performance of random forest better.