Question

我适合我的渐变增强模型，并尝试打印变量重要性。我使用相同的代码，并使用随机森林。运行varImp（）时我一直收到错误。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")

Answer 1

你是否包括图书馆＆＃34; gbm？＆＃34;这为我解决了同样的错误。

Answer 2

谢谢，这也对我有用：

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
.......

梯度提升变量重要性

2 个答案: