Building a random forest model
confusion
REGRESSION
Using R4.2.3 system, 1006 samples were divided into training sample set (The training sample set uses the random forest algorithm to construct a random forest model based on independent risk factors (age, CD117, P2X3R, NGF, TrkA) obtained from logistic regression analysis. ntree is 20, and variables mtry in each tree is 1. We evaluate the classification performance of the model by calculating the confusion matrix between the training and testing sets, and then analyze the calibration curve and receiver operating characteristic curve. Finally, in order to compare whether the AUC differences between different variables are statistically significant, Delong tests were performed on each variable in the training and testing sets.