基于RFECV特征选择和随机森林预测模型的应用与优化

Feature Selection Based on RFECV and Application and Optimization of Random Forest Prediction Model

该文基于随机森林预测模型,提出RFECV特征选择方法:首先对特征变量进行独热编码,再利用RFECV内置的交叉验证评估各特征子集性能,以确定最佳特征数量,并递归消除低重要性特征。实验表明,该方法在随机森林上训练与预测更快,均方误差更低,特征提取准确率高。

Based on the random forest prediction model,this paper proposes the RFECV feature selection method:firstly,the feature variables are encoded with one-hot encoding,and then the built-in cross-validation of RFECV is used to evaluate the performance of each feature subset to determine the optimal number of features,and recursively eliminate low-importance features.Experiments show that this method achieves faster training and prediction on the random forest,lower mean squared error,and high accuracy in feature extraction.