我正在尝试理解并在Kaggle上的数据集(https://www.kaggle.com/uciml/breast-cancer-wisconsin-data)中实现R语言的单类分类。
在尝试打印混淆矩阵时，出现了以下错误：
Error in! All.equal (nrow (data), ncol (data)): invalid type argument
我做错了什么？
library(caret)library(dplyr)library(e1071)library(NLP)library(tm)library(data.table)ds = read.csv('C:/Users/hugos/Desktop/FS Dataset/Health/data_cancer.csv', header = TRUE)mycols <- c("id","diagnosis","radius_mean","texture_mean","perimeter_mean","area_mean", "smoothness_mean","compactness_mean","concavity_mean", "concave.points_mean","symmetry_mean","fractal_dimension_mean", "radius_se","texture_se","perimeter_se", "area_se","smoothness_se","compactness_se", "concavity_se","concave.points_se","symmetry_se", "fractal_dimension_se","radius_worst","texture_worst", "perimeter_worst","area_worst","smoothness_worst", "compactness_worst","concavity_worst","concave.points_worst", "symmetry_worst","fractal_dimension_worst")#Convert to numericsetDT(ds)[, (mycols) := lapply(.SD, as.numeric), .SDcols = mycols]#Convert classification to logicaldata <- ds[,.(id,radius_mean,texture_mean,perimeter_mean,area_mean,smoothness_mean,compactness_mean,concavity_mean,concave.points_mean,symmetry_mean,fractal_dimension_mean,radius_se,texture_se,perimeter_se,area_se,smoothness_se,compactness_se,concavity_se,concave.points_se,symmetry_se,fractal_dimension_se,radius_worst,texture_worst,perimeter_worst,area_worst,smoothness_worst,compactness_worst,concavity_worst,concave.points_worst,symmetry_worst,fractal_dimension_worst,diagnosis = ds$diagnosis == "TRUE")]dataclean <- na.omit(data)#Separating train and testinTrain<-createDataPartition(1:nrow(dataclean),p=0.7,list=FALSE)train<- dataclean[inTrain]test <- dataclean[-inTrain]svm.model<-svm(diagnosis ~ id+radius_mean+texture_mean+perimeter_mean+area_mean+smoothness_mean+compactness_mean+concavity_mean+concave.points_mean+symmetry_mean+fractal_dimension_mean+radius_se+texture_se+perimeter_se+area_se+smoothness_se+compactness_se+concavity_se+concave.points_se+symmetry_se+fractal_dimension_se+radius_worst+texture_worst+perimeter_worst+area_worst+smoothness_worst+compactness_worst+concavity_worst+concave.points_worst+symmetry_worst+fractal_dimension_worst, data = train, type='one-classification', trControl = fitControl, nu=0.10, scale=TRUE, kernel="radial", metric = "ROC")#Perform predictions svm.predtrain<-predict(svm.model,train)svm.predtest<-predict(svm.model,test)confTrain <- table(Predicted=svm.predtrain, Reference=train$diagnosis[as.integer(names(svm.predtrain))])confTest <- table(Predicted=svm.predtest, Reference=test$diagnosis[as.integer(names(svm.predtest))])confusionMatrix(confTest,positive='TRUE')print(confTrain)print(confTest)回答：
你的问题出在这一行：
#Convert classification to logicaldata <- ds[, .(id, radius_mean, ..., diagnosis = ds$diagnosis == "TRUE")]我假设你使用的是R 4.0版本，因为read.csv函数的默认行为现在不会将字符列转换为因子。这个命令：
#Convert to numericsetDT(ds)[, (mycols) := lapply(.SD, as.numeric), .SDcols = mycols]会将所有诊断结果转换为NA，因为它们是”M”或”B”，分别代表恶性和良性。
因此，确保在导入数据时将字符串转换为因子。
ds = read.csv('.../data_cancer.csv', header = TRUE, stringsAsFactors = TRUE)str(ds)'data.frame': 569 obs. of 33 variables: $ id : int 842302 842517 84300903 84348301 84358402 843786 844359 ... $ diagnosis : Factor w/ 2 levels "B","M": 2 2 2 2 2 2 2 2 2 2 ...我想有些人需要一段时间来适应R的新行为。你转换分类为逻辑值的命令应该改为：
data <- ds[, .(id, radius_mean, ..., diagnosis = diagnosis == 2)] # 或者 == 1 ?这样，你的其他命令就能正常工作了。
confusionMatrix(confTest, positive='TRUE')Confusion Matrix and Statistics ReferencePredicted FALSE TRUE FALSE 10 8 # Note these numbers may change TRUE 100 50 Accuracy : 0.3571 95% CI : (0.2848, 0.4346) No Information Rate : 0.6548 P-Value [Acc > NIR] : 1 Kappa : -0.0342 Mcnemar's Test P-Value : <2e-16 Sensitivity : 0.86207 Specificity : 0.09091 Pos Pred Value : 0.33333 Neg Pred Value : 0.55556 Prevalence : 0.34524 Detection Rate : 0.29762 Detection Prevalence : 0.89286 Balanced Accuracy : 0.47649 'Positive' Class : TRUE