梯度升压变量重要性

Question

我已经拟合我的梯度提升模型，并试图打印变量的重要性。我使用了相同的代码，并使用随机森林工作。在运行varImp()时，我一直会收到错误。错误如下。

代码$varImp中的错误(object$finalModel，.)：找不到函数"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")

Answer 1

您是否包括了"gbm?“(library(gbm))库，它为我修复了相同的错误。

Answer 2

谢谢，这也是我的工作：

library(gbm)
gbmFitGene=train(StatoP~.,data=dataSetGeneExp, method ="gbm" )
vImpGbm=varImp(gbmFitGene) #Variable importance
>
gbm variable importance
only 20 most important variables shown (out of 16600)
           Overall
MRPL51     100.00
LOC646200   60.16
UQCRB       42.09
.......