Seems like you are trying to do classification on a character dependent variable. Let's say we use this fantastic dataset from kaggle:
library(randomForest)
train_data = read.csv("credit_train.csv",stringsAsFactors=FALSE)
str(train_data)
'data.frame': 808 obs. of 17 variables:
$ Class : chr "Good" "Bad" "Good" "Good" ...
$ Duration : int 6 48 12 36 24 12 30 48 12 24 ...
$ Amount : int 1169 5951 2096 9055 2835 3059 5234 4308 1567 1199 ...
$ InstallmentRatePercentage : int 4 2 2 2 3 2 4 3 1 4 ...
$ ResidenceDuration : int 4 2 3 4 4 4 2 4 1 4 ...
$ Age : int 67 22 49 35 53 61 28 24 22 60 ...
$ NumberExistingCredits : int 2 1 1 1 1 1 2 1 1 2 ...
$ NumberPeopleMaintenance : int 1 1 2 2 1 1 1 1 1 1 ...
$ Telephone : int 0 1 1 0 1 1 1 1 0 1 ...
$ ForeignWorker : int 1 1 1 1 1 1 1 1 1 1 ...
$ CheckingAccountStatus.lt.0 : int 1 0 0 0 0 0 0 1 0 1 ...
$ CheckingAccountStatus.0.to.200: int 0 1 0 0 0 0 1 0 1 0 ...
$ CheckingAccountStatus.gt.200 : int 0 0 0 0 0 0 0 0 0 0 ...
$ CreditHistory.ThisBank.AllPaid: int 0 0 0 0 0 0 0 0 0 0 ...
$ CreditHistory.PaidDuly : int 0 1 0 1 1 1 0 1 1 0 ...
$ CreditHistory.Delay : int 0 0 0 0 0 0 0 0 0 0 ...
$ CreditHistory.Critical : int 1 0 1 0 0 0 1 0 0 1 ...
fit <- randomForest(Class~. ,data = train_data)
Error in y - ymean : non-numeric argument to binary operator
In addition: Warning messages:
1: In randomForest.default(m, y, ...) :
The response has five or fewer unique values. Are you sure you want to do regression?
2: In mean.default(y) : argument is not numeric or logical: returning NA
You can see I get the same error. Your dependent variable is a character. we convert it into a factor and it works:
train_data$Class = factor(train_data$Class)
fit <- randomForest(Class~. ,data = train_data)
