如何生成嵌套ifelse()陳述句來評估R中混淆矩陣的三個分類二進制級別的預測-有解無憂

問題：

我有一個名為的預測模型的匯總輸出結果，該模型pred_model_Tree1 (three columns; 6 obs; 3 variables)是由我使用使用and包的gbm方法構建的分類器生成的。這是通過預測我用 ( )呼叫的分類模型的結果（參見下面的 R 代碼）產生的。Caretgbmpred_model_Tree1gbmFit1test.datatest.data$Country

我想撰寫一個nested ifelse()模型來評估預測，并且我想使用評估結果confusionMatrix()從e1701 pacakge.

我的資料有nine continuous independent variables，一個dependent variable叫做“國家”。這個問題也與我在這里問的一個問題有關。我之前遇到過一條錯誤訊息，可以通過我上一個問題中的鏈接查看。這是混淆矩陣的輸出錯誤。

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

因此，我需要評估模型中的每個預測，這樣......

#i.e. if the predicted likelihood that the country is France is '0.9'and the likelihood #it's Holland is '0.1', then the prediction is "France"

#Example
# Evaluate each prediction, i.e. if the predicted likelihood that the country is France is '0.9'
# and the likelihood it's Holland is '0.1', then the prediction is "France"
pred_model_Tree1$evaluation <- ifelse(pred_model_Tree1$France >= 0.5, "France", "Holland")

# Now you can print the confusionMatrix (make sure each factor has the same levels)
confusionMatrix(factor(pred_model_Tree1$evaluation, levels = unique(test.data$Country)),
                factor(test.data$Country, levels = unique(test.data$Country)))

但是，我three categorical binary levels的dependent variable預測資料有“法國”、“意大利”和“西班牙”。因此，我假設我需要nested ifelse()為三元評估撰寫一個陳述句。

如何應用此邏輯來評估可進一步用于混淆矩陣的預測，以輸出分類器模型準確性的匯總統計資訊？由于存在三個級別，我是否還需要使用 >= 大于或等于 0.33 的值？我試圖使用嵌套的 ifelse 函式，如下所示：

 pred_model_Tree1$evaluation <- ifelse(pred_model_Tree1$France >= 0.5, "France", "Holland",
                                   ifelse(pred_model_Tree1$France >= 0.5, "France", "Spain",
                                   ifelse(pred_model_Tree1$Spain >= 0.5,"Spain", "Holland")))
    
    pred_model_Tree1$evaluation

#The output results only show two countries, not three
1] "France"  "France"  "Holland" "Holland" "Holland" "France"

期望結果：pred_model_Tree1$evaluation輸出應包含三個國家/地區，在評估預測后重復 3 次，因此的級別和結構與pred_model_Tree1$evaluation的預測輸出相同pred_model_Tree1 model。

1] "France"  "France"  "Holland" "Holland" "Holland" "France" "Spain" "Spain "Spain"

與往常一樣，我非常感謝伸出援助之手。

R代碼

#install packages
library(gbm)
library(caret)
library(e1701)

set.seed(45L)

#Produce a new version of the data frame 'Clusters_Dummy' with the rows shuffled
NewClusters=Cluster_Dummy_2[sample(1:nrow(Cluster_Dummy_2)),]

#Produce a dataframe
NewCluster<-as.data.frame(NewClusters)

#Split the training and testing data 70:30
training.parameters <- Cluster_Dummy_2$Country %>% 
createDataPartition(p = 0.7, list = FALSE)
train.data <- NewClusters[training.parameters, ]
test.data <- NewClusters[-training.parameters, ]

dim(train.data)
#259  10

dim(test.data)
#108  10

#Auxiliary function for controlling model fitting
#10 fold cross validation; 10 times
fitControl <- trainControl(## 10-fold CV
                          method = "repeatedcv",
                          number = 10,
                          ## repeated ten times
                          repeats = 10,
                          classProbs = TRUE)
#Fit the model
gbmFit1 <- train(Country ~ ., data=train.data, 
                 method = "gbm", 
                 trControl = fitControl,
                 ## This last option is actually one
                 ## for gbm() that passes through
                 verbose = FALSE)
gbmFit1
summary(gbmFit1)

#Predict the model with the test data
pred_model_Tree1 = predict(gbmFit1, newdata = head(test.data$Country), type = "prob")
pred_model_Tree1

print(pred_model_Tree1)

#Attempt at a nested ifelse() statement

# Evaluate each prediction, i.e. if the predicted likelihood that the country is France is '0.9'
# and the likelihood it's Holland is '0.1', then the prediction is "France"
#>= greater than or equal to
pred_model_Tree1$evaluation <- ifelse(pred_model_Tree1$France >= 0.5, "France", "Holland",
                               ifelse(pred_model_Tree1$France >= 0.5, "France", "Spain",
                               ifelse(pred_model_Tree1$Spain >= 0.5,"Spain", "Holland")))

pred_model_Tree1$evaluation

#The problem is here. The output should contain three countries repeated three times so the levels and structure of the prediction evaluation are the same as the predicted output.

1] "France"  "France"  "Holland" "Holland" "Holland" "France" 

# Now you can print the confusionMatrix (make sure each factor has the same levels)
confusionMatrix(factor(pred_model_Tree1$evaluation, levels = unique(test.data$Country)),
                factor(test.data$Country, levels = unique(test.data$Country)))

資料

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13128104L, 1692L, 8581L, 23L, 5874213L, 4550L, 644668065L, 3712371L, 
5928L, 8833L, 7L, 2186023L, 61627221L, 37297L, 716427989L, 21387L
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123367978L, 818775L, 123745614L, 25345654L, 3L), Country = c("Holland", 
"Holland", "Holland", "Holland", "Holland", "Holland", "Spain", 
"Spain", "Spain", "Spain", "Spain", "Spain", "Spain", "Spain", 
"Spain", "Spain", "Spain", "Spain", "Holland", "Holland", "Holland", 
"Holland", "Holland", "Holland", "France", "France", "France", 
"France", "France", "France", "France", "France", "France", "France", 
"France", "Spain", "Spain", "Spain", "Spain", "Spain", "Spain", 
"Spain", "Spain", "France", "France", "France", "France", "Holland", 
"Holland", "Holland", "Holland", "Holland", "Holland", "Holland", 
"Holland", "Holland", "Holland", "Holland", "Holland", "Holland", 
"Spain", "Spain", "Spain", "Spain", "Spain", "Spain", "Spain", 
"Holland", "Holland", "Holland", "Holland", "France", "France", 
"France", "France", "France", "France", "France", "Spain", "Spain", 
"Spain", "Spain", "Spain", "Spain", "Spain", "Spain", "Spain", 
"Spain", "Spain", "Spain", "Spain", "Spain", "Spain", "Spain", 
"Spain", "Spain", "France", "France", "France")), row.names = c(NA, 
99L), class = "data.frame")

uj5u.com熱心網友回復：

您可以為每個觀察選擇具有最高預測概率的國家：

pred_model_Tree1 = predict(gbmFit1, newdata = test.data, type = "prob")

pred_model_Tree1$evaluation <- names(pred_model_Tree1)[apply(pred_model_Tree1, 1, which.max)]

table(pred_model_Tree1$evaluation)

這使：

 France Holland   Spain 
      1      11      16

然后該confusionMatrix()函式使用給定的代碼。

轉載請註明出處，本文鏈接：https://www.uj5u.com/qiye/485416.html

標籤：r if 语句机器学习分类混淆矩阵

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