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[英]Error: `data` and `reference` should be factors with the same levels. Confusion matrix for Logistic Regression
[英]Confusion Matrix Error: Error: `data` and `reference` should be factors with the same levels
我目前正在尝试建立神经网络,以预测数据中人员的排名。
等级系统为:A,B,C,D,E
一切都非常顺利,直到我进入混乱的矩阵。 我收到错误“错误: data
和reference
应该是具有相同水平的因子”。 我在其他帖子上尝试了许多不同的方法,但似乎都没有用。
NNPredicitions和test $ Rank中的级别相同。 我用table()检查了它们。
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(test $ Rank)
长度(NNPredictions)[1] 98长度(test $ Rank)[1] 98
table(NNPredictions,test $ Rank,useNA =“ ifany”)NNPredictions ABCDEA 1 0 0 0 0 B 0 6 0 0 0 C 0 0 11 0 0 D 0 0 0 18 18 E 0 0 0 0 62
还将method =“ prob”更改为method =“ raw”
Table1 <-table(NNPredictions,test $ Rank,useNA =“ ifany”)
cnf1 <-confusionMatrix(表1)
由dclarson提供
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