Feature Selection Based on RFECV and Application and Optimization of Random Forest Prediction Model
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.
random forest prediction modelone-hot encodingrecursive feature eliminationcross-validation
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