我试图比较标准的神经网络方法和基于 ROC 度量的极限学习机分类器,使用 R 包中的 "nnet"
和 "elm"
方法来进行比较。对于 nnet,一切都工作正常,但是当使用 method = "elm"
时,会出现以下错误:
Error in evalSummaryFunction(y, wts = weights, ctrl = trControl, lev = classLevels, :
train()'s use of ROC codes requires class probabilities. See the classProbs option of trainControl()
In addition: Warning messages:
1: In train.default(x, y, weights = w, ...) :
At least one of the class levels are not valid R variables names; This may cause errors if class probabilities are generated because the variables names will be converted to: X1, X2
2: In train.default(x, y, weights = w, ...) :
Class probabilities were requested for a model that does not implement them
当method = "nnet"
时,我也遇到了第一个错误,但是我通过将分数设置为因子变量来解决了问题。因此,这不可能是问题所在。
我对R相对较新,也许错误很琐碎,但现在我卡住了...由于elmNN似乎是相对较新的实现,我在网上也找不到有关如何在caret
中使用elm的任何信息。
gc <- read.table("germanCreditNum.txt")
colnames(gc)[25]<-"score"
gc_inTrain <- createDataPartition(y = gc$score,
## the outcome data are needed
p = .8,
## The percentage of data in the
## training set
list = FALSE)
str(gc_inTrain)
gc_training <- gc[ gc_inTrain,]
gc_testing <- gc[-gc_inTrain,]
nrow(gc_training) ## No of rows
nrow(gc_testing)
gc_training$score <- as.factor(gc_training$score)
gc_ctrl <- trainControl(method = "boot",
repeats = 1,
classProbs = TRUE,
summaryFunction = twoClassSummary)
neuralnetFit <- train(score ~ .,
data = gc_training,
method = "nnet",
trControl = gc_ctrl,
metric = "ROC",
preProc = c("center", "scale"))
neuralnetFit
plot(neuralnetFit)
nnClasses <- predict(neuralnetFit, newdata = gc_testing)
str(nnClasses)
## start with ELM for German Credit
gc_ctrl2 <- trainControl(classProbs = TRUE, summaryFunction = twoClassSummary)
elmFit <- train(score ~ .,
data = gc_training,
method = "elm",
trControl = gc_ctrl2,
metric = "ROC",
preProc = c("center", "scale"))
elmFit
plot(elmFit)
elmClasses <- predict(elmFit, newdata = gc_testing)
str(elmClasses)
elmProbs <- predict(elmFit, newdata = gc_testing, type = "prob")
head(elmProbs)