错误:“数据”和“参考”应该是具有相同水平的因子

时间:2018-07-26 23:44:46

标签: r machine-learning classification

尝试使用RandomForest预测模型的准确性,但遇到以下错误。
    错误:datareference应该是具有相同水平的因子。

这是以下代码

rfModel <- randomForest(Churn ~., data = training)
print(rfModel)
pred_rf <- predict(rfModel, testing)
caret::confusionMatrix(pred_rf, testing$Churn)
testing$Churn

训练和测试数据按7:3的比例分割

在运行代码时也收到以下警告

Warning messages:
1: In get(results[[i]], pos = which(search() == packages[[i]])) :
  restarting interrupted promise evaluation
2: In get(results[[i]], pos = which(search() == packages[[i]])) :
  internal error -3 in R_decompress1

测试数据的结构

str(testing)
'data.frame':   999 obs. of  18 variables:
 $ account_length        : int  84 75 147 141 65 62 85 93 76 73 ...
 $ International.plan    : Factor w/ 2 levels "No","Yes": 2 2 2 2 1 1 1 1 1 1 ...
 $ Voice.mail.plan       : Factor w/ 2 levels "No","Yes": 1 1 1 2 1 1 2 1 2 1 ...
 $ Number.vmail.messages : int  0 0 0 37 0 0 27 0 33 0 ...
 $ Total.day.minutes     : num  299 167 157 259 129 ...
 $ Total.day.calls       : int  71 113 79 84 137 70 139 114 66 90 ...
 $ Total.day.charge      : num  50.9 28.3 26.7 44 21.9 ...
 $ Total.eve.minutes     : num  61.9 148.3 103.1 222 228.5 ...
 $ Total.eve.calls       : int  88 122 94 111 83 76 90 111 65 88 ...
 $ Total.eve.charge      : num  5.26 12.61 8.76 18.87 19.42 ...
 $ Total.night.minutes   : num  197 187 212 326 209 ...
 $ Total.night.calls     : int  89 121 96 97 111 99 75 121 108 74 ...
 $ Total.night.charge    : num  8.86 8.41 9.53 14.69 9.4 ...
 $ Total.intl.minutes    : num  6.6 10.1 7.1 11.2 12.7 13.1 13.8 8.1 10 13 ...
 $ Total.intl.calls      : int  7 3 6 5 6 6 4 3 5 2 ...
 $ Total.intl.charge     : num  1.78 2.73 1.92 3.02 3.43 3.54 3.73 2.19 2.7 3.51 ...
 $ Customer.service.calls: int  2 3 0 0 4 4 1 3 1 1 ...
 $ Churn                 : chr  "0" "0" "0" "0" ...

训练集的结构相同,观察2334次

pred_rf的结构

 str(pred_rf)
 Factor w/ 2 levels "FALSE","TRUE": 1 1 1 1 2 2 1 1 1 1 ...
 - attr(*, "names")= chr [1:999] "4" "5" "8" "10" ...

请帮帮我。

1 个答案:

答案 0 :(得分:1)

好的,我也遇到了同样的问题,并弄清楚了。

查看您的str(testing),请注意您的客户流失不是因素,而是 chr

首先,您需要将流失率设置为一个因子

Churn <- as.factor(testing$Churn)

再次检查您的str(testing),以查看它实际上是否已更改。

现在您可以使用:

test_predictions = predict(rf_model, testing_set)
test_predictions

conf_matrix = confusionMatrix(test_predictions, Churn)
conf_matrix

请参阅:https://community.rstudio.com/t/how-to-deal-with-rlang-errors/27248