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我正在尝试理解和实现分类器 R 中的一个类基于多个 UCI 和其中之一（http://archive.ics.uci.edu/ml/datasets/Chronic_Kidney_Disease）。

尝试打印混淆矩阵时，您会给出错误“所有参数必须具有相同的长度”。

我究竟做错了什么？

library(caret)
library(dplyr)
library(e1071)
library(NLP)
library(tm)

ds = read.csv('kidney_disease.csv', 
              header = TRUE)

#Remover colunas inutiliz?veis              
ds <- subset(ds, select = -c(age), classification =='ckd' )

x <- subset(ds, select = -classification) #make x variables
y <- ds$classification #make y variable(dependent)

# test on the whole set
#pred <- predict(model, subset(ds, select=-classification))


trainPositive<-x
testnegative<-y

inTrain<-createDataPartition(1:nrow(trainPositive),p=0.6,list=FALSE)

trainpredictors<-trainPositive[inTrain,1:4]
trainLabels<-trainPositive[inTrain,6]

testPositive<-trainPositive[-inTrain,]
testPosNeg<-rbind(testPositive,testnegative)

testpredictors<-testPosNeg[,1:4]
testLabels<-testPosNeg[,6]

svm.model<-svm(trainpredictors,y=NULL,
               type='one-classification',
               nu=0.10,
               scale=TRUE,
               kernel="radial")

svm.predtrain<-predict(svm.model,trainpredictors)
svm.predtest<-predict(svm.model,testpredictors)

# confusionMatrixTable<-table(Predicted=svm.pred,Reference=testLabels)
# confusionMatrix(confusionMatrixTable,positive='TRUE')

confTrain <- table(Predicted=svm.predtrain,Reference=trainLabels)
confTest <- table(Predicted=svm.predtest,Reference=testLabels)

confusionMatrix(confTest,positive='TRUE')


print(confTrain)
print(confTest)

#grid

以下是我正在使用的数据集的一些第一行：

 id bp    sg al su    rbc       pc        pcc         ba bgr bu  sc sod pot hemo pcv   wc
1  0 80 1.020  1  0          normal notpresent notpresent 121 36 1.2  NA  NA 15.4  44 7800
2  1 50 1.020  4  0          normal notpresent notpresent  NA 18 0.8  NA  NA 11.3  38 6000
3  2 80 1.010  2  3 normal   normal notpresent notpresent 423 53 1.8  NA  NA  9.6  31 7500
4  3 70 1.005  4  0 normal abnormal    present notpresent 117 56 3.8 111 2.5 11.2  32 6700
5  4 80 1.010  2  0 normal   normal notpresent notpresent 106 26 1.4  NA  NA 11.6  35 7300
6  5 90 1.015  3  0                 notpresent notpresent  74 25 1.1 142 3.2 12.2  39 7800
   rc htn  dm cad appet  pe ane classification
1 5.2 yes yes  no  good  no  no            ckd
2      no  no  no  good  no  no            ckd
3      no yes  no  poor  no yes            ckd
4 3.9 yes  no  no  poor yes yes            ckd
5 4.6  no  no  no  good  no  no            ckd
6 4.4 yes yes  no  good yes  no            ckd

错误日志：

> confTrain <- table (Predicted = svm.predtrain, Reference = trainLabels)
Table error (Predicted = svm.predtrain, Reference = trainLabels):
all arguments must be the same length
> confTest <- table (Predicted = svm.predtest, Reference = testLabels)
Table error (expected = svm.predtest, reference = testLabels):
all arguments must be the same length
>
> confusionMatrix (confTest, positive = 'TRUE')
ConfusionMatrix error (confTest, positive = "TRUE"):
'confTest' object not found
>
>
> print (confTrain)
Printing error (confTrain): object 'confTrain' not found
> print (confTest)
Printing error (confTest): object 'confTest' not found
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1 回答 1

1

我看到了很多问题。首先，您的很多数据似乎都是类字符而不是数字，这是分类器所要求的。让我们选择一些列并转换为数字。我会用data.table，因为fread很方便。

library(caret)
library(e1071)
library(data.table)
setDT(ds)
#Choose columns
mycols <- c("id","bp","sg","al","su")
#Convert to numeric
ds[,(mycols) := lapply(.SD, as.numeric),.SDcols = mycols]

#Convert classification to logical
data <- ds[,.(bp,sg,al,su,classification = ds$classification == "ckd")]
data
     bp    sg al su classification
  1: 80 1.020  1  0           TRUE
  2: 50 1.020  4  0           TRUE
  3: 80 1.010  2  3           TRUE
  4: 70 1.005  4  0           TRUE
  5: 80 1.010  2  0           TRUE
 ---                              
396: 80 1.020  0  0          FALSE
397: 70 1.025  0  0          FALSE
398: 80 1.020  0  0          FALSE
399: 60 1.025  0  0          FALSE
400: 80 1.025  0  0          FALSE

清理数据后，您可以使用createDataPartition原始代码中的训练和测试集进行采样。

#Sample data for training and test set
inTrain<-createDataPartition(1:nrow(data),p=0.6,list=FALSE)
train<- data[inTrain,]
test <- data[-inTrain,]

然后我们可以创建模型并进行预测。

svm.model<-svm(classification ~ bp + sg + al + su, data = train,
               type='one-classification',
               nu=0.10,
               scale=TRUE,
               kernel="radial")

#Perform predictions 
svm.predtrain<-predict(svm.model,train)
svm.predtest<-predict(svm.model,test)

您对交叉表的主要问题是该模型只能预测没有任何NAs 的案例，因此您必须将分类级别子集到具有预测的那些级别。然后你可以评估confusionMatrix：

confTrain <- table(Predicted=svm.predtrain,
                   Reference=train$classification[as.integer(names(svm.predtrain))])
confTest <- table(Predicted=svm.predtest,
                  Reference=test$classification[as.integer(names(svm.predtest))])

confusionMatrix(confTest,positive='TRUE')

Confusion Matrix and Statistics

         Reference
Predicted FALSE TRUE
    FALSE     0   17
    TRUE     55   64

               Accuracy : 0.4706         
                 95% CI : (0.3845, 0.558)
    No Information Rate : 0.5956         
    P-Value [Acc > NIR] : 0.9988         

                  Kappa : -0.2361        

 Mcnemar's Test P-Value : 1.298e-05      

            Sensitivity : 0.7901         
            Specificity : 0.0000         
         Pos Pred Value : 0.5378         
         Neg Pred Value : 0.0000         
             Prevalence : 0.5956         
         Detection Rate : 0.4706         
   Detection Prevalence : 0.8750         
      Balanced Accuracy : 0.3951         

       'Positive' Class : TRUE

数据

library(archive)
library(data.table)
tf1 <- tempfile(fileext = ".rar")
#Download data file
download.file("http://archive.ics.uci.edu/ml/machine-learning-databases/00336/Chronic_Kidney_Disease.rar", tf1)
tf2 <- tempfile()
#Un-rar file
archive_extract(tf1, tf2)
#Read in data
ds <- fread(paste0(tf2,"/Chronic_Kidney_Disease/chronic_kidney_disease.arff"), fill = TRUE, skip = "48")
#Remove erroneous last column
ds[,V26:= NULL]
#Set column names (from header)
setnames(ds,c("id","bp","sg","al","su","rbc","pc","pcc","ba","bgr","bu","sc","sod","pot","hemo","pcv","wc","rc","htn","dm","cad","appet","pe","ane","classification"))
#Replace "?" with NA
ds[ds == "?"] <- NA
于 2020-05-12T03:18:21.243 回答