小编Dus*_*ith的帖子

我已经适应了我的梯度提升模型，并试图打印出可变的重要性。我使用了相同的代码，并使用随机森林。运行varImp（）时，我不断收到错误消息。错误如下。

Error in code$varImp(object$finalModel, ...) : 
  could not find function "relative.influence"

#Split into testing and training
set.seed(7)
Data_Splitting <- createDataPartition(clean_data$Output,p=2/3,list=FALSE)
training = clean_data[Data_Splitting,]
testing = clean_data[-Data_Splitting,]

#Random Forest training part
set.seed(7)
gbm_train <- train(Output~., data=training, method = "gbm", 
                   trControl = trainControl(method="cv",number=4,classProbs = T,summaryFunction = twoClassSummary),metric="ROC")

#Plot of variable importance
varImp(gbm_train)
summary.gbm(gbm_train)
plot(varImp(gbm_train))
print(gbm)

#Random Forest Testing phase
gbm_predict = predict(gbm_train,newdata=testing,type="prob")