R中这一行代码中的错误可能是什么？

Question

fit <- randomForest(class~. ,data = train_data)

有人能告诉我这行代码有什么问题吗？

这里，train_data是用来预测收入>50k或<50k的训练数据，我在这一行中得到的误差是：

除了警告消息:在randomForest.default(m，y，.)中，y- ymean中的错误:二进制运算符的非数值参数： 响应有五个或更少的唯一值。您确定要进行回归吗? 2:在mean.default(y)：参数不是数字或逻辑:返回NA

Answer 1

似乎您正在尝试对字符因变量进行分类。假设我们使用这个来自kaggle的奇妙的数据集：

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

你可以看到我也犯了同样的错误。因变量是一个字符。我们把它转化为一个因素，它起作用：

train_data$Class = factor(train_data$Class)

fit <- randomForest(Class~. ,data = train_data)

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