关于 r:Confusion Matrix Error: Error: `data` 和 `reference` 应该是相同级别的因子

Confusion Matrix Error: Error: `data` and `reference` should be factors with the same levels

我目前正在尝试构建一个神经网络来预测人们在数据中的排名。

等级系统为：A,B,C,D,E

在我到达我的混淆矩阵之前，一切都运行得非常顺利。我收到错误"错误：data 和 reference 应该是具有相同级别的因素。"。我在其他帖子上尝试了许多不同的方法，但似乎都没有。

NNPredicitions 和 test$Rank 中的级别都相同。我用 table() 检查了它们。

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library(readxl)
library(caret)
library(neuralnet)
library(forecast)
library(tidyverse)
library(ggplot2)

Indirect <-read_excel("C:/Users/Abdulazizs/Desktop/Projects/Indirect/FIltered Indirect.xlsx",
n_max = 500)

Indirect$Direct_or_Indirect <- NULL

Indirect$parentaccount <- NULL

sum(is.na(Indirect))

counts <- table(Indirect$Rank)

barplot(counts)

summary(counts)

part2 <- createDataPartition(Indirect$Rank, times = 1, p = .8, list = FALSE, groups = min(5, length(Indirect$Rank)))

train <- Indirect[part2, ]
test <- Indirect[-part2, ]

set.seed(1234)

TrainingParameters <- trainControl(method ="repeatedcv", number = 10, repeats=10)

as.data.frame(train)
as.data.frame(test)

NNModel <- train(train[,-7], train$Rank,
method ="nnet",
trControl= TrainingParameters,
preProcess=c("scale","center"),
na.action = na.omit
)

NNPredictions <-predict(NNModel, test, type ="raw")

summary(NNPredictions)

confusionMatrix(NNPredictions, test$Rank)

长度(NNPredictions)
长度(测试$排名)

length(NNPredictions)
[1] 98
length(test$Rank)
[1] 98

table(NNPredictions, test$Rank, useNA="ifany")
NN预测 A B C D E
一个 1 0 0 0 0
乙 0 6 0 0 0
C 0 0 11 0 0
D 0 0 0 18 0
E 0 0 0 0 62