variables - 梯度提升变量重要性

Question

我已经适合我的梯度提升模型，并正在尝试打印变量重要性。我使用了相同的代码并使用随机森林工作。运行时我不断收到错误消息varImp()。错误如下。

代码$varImp(object$finalModel, ...) 中的错误：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

您是否包含了库“gbm”？( library(gbm)) 这为我解决了同样的错误。

Answer 2

谢谢，这对我也有用：

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
.......