尝试使用RandomForest预测模型的准确性,但遇到以下错误。
错误:data
和reference
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。
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