【发布时间】:2015-08-17 17:35:15
【问题描述】:
我试图更多地了解caret 包,但遇到了一个我不确定如何解决的障碍。
#loading up libraries
library(MASS)
library(caret)
library(randomForest)
data(survey)
data<-survey
#create training and test set
split <- createDataPartition(data$W.Hnd, p=.8)[[1]]
train<-data[split,]
test<-data[-split,]
#creating training parameters
control <- trainControl(method = "cv",
number = 10,
p =.8,
savePredictions = TRUE,
classProbs = TRUE,
summaryFunction = "twoClassSummary")
#fitting and tuning model
tuningGrid <- data.frame(.mtry = floor(seq(1 , ncol(train) , length = 6)))
rf_tune <- train(W.Hnd ~ . ,
data=train,
method = "rf" ,
metric = "ROC",
trControl = control)
不断出现错误:
Error in evalSummaryFunction(y, wts = weights, ctrl = trControl, lev = classLevels, :
attempt to apply non-function
我已确认我的 DV (W.Hnd) 是一个因子水平,因此随机森林适合用于分类。我的假设是caret 不知道适用于randomForest 算法?除此之外我不知道。
【问题讨论】:
标签: r machine-learning r-caret