R中的Caret和KNN：预测函数给出错误

Question

I try to predict with a simplified KNN model using the caret package in R. It always gives the same error, even in the very simple reproducible example here:

library(caret)
set.seed(1)

#generate training dataset "a" 
n = 10000
a = matrix(rnorm(n*8,sd=1000000),nrow = n)
y = round(runif(n))
a = cbind(y,a)
a = as.data.frame(a)
a[,1] = as.factor(a[,1])
colnames(a) = c("y",paste0("V",1:8))

#estimate simple KNN model
ctrl <- trainControl(method="none",repeats = 1)
knnFit <- train(y ~ ., data = a, method = "knn", trControl = ctrl, preProcess = c("center","scale"),  tuneGrid = data.frame(k = 10))

#predict on the training dataset (=useless, but should work)
knnPredict <- predict(knnFit,newdata = a,  type="prob")

This gives

Error in [.data.frame (out, , obsLevels, drop = FALSE) : undefined columns selected

Defining a more realistic test dataset "b" without the target variable y...

#generate test dataset
b =  matrix(rnorm(n*8,sd=1000000),nrow = n) 
b = as.data.frame(b)
colnames(b) = c(paste0("V",1:8))

#predict on the test datase
knnPredict <- predict(knnFit,newdata = b,  type="prob")

gives the same error

Error in [.data.frame (out, , obsLevels, drop = FALSE) : undefined columns selected

I know that the columnames are important, but here they are identical. What is wrong here? Thanks!

Answer 1

The problem is your y variable. When you are asking for the class probabilities, the train and / or the predict function puts them into a data frame with a column for each class. If the factor levels are not valid variable names, they are automatically changed (eg "0" becomes "X0"). See also this post .

If you change this line in your code it should work:

a[,1] = factor(a[,1], labels = c("no", "yes"))