tapply (var, y, mean, na.rm = TRUE) 오류 : 인수의 길이가 같아야합니다.

이 오류가 발생하는 이유는 무엇입니까? 은 무슨 뜻인가요? 나는 그 방법을 사용하지 않았습니까?

오류 발생 : naive_model <-naiveBayes (X_train, Y_train)

오류:

Error in tapply(var, y, mean, na.rm = TRUE) : 
  arguments must have same length

암호:

  library(e1071)
  #Naive Bayes
  #Learn Time
  start.time <- Sys.time()
  naive_model <-naiveBayes(X_train,Y_train)
  end.time <- Sys.time()
  time.taken <- end.time - start.time
  naivebayes_Learnruntime[i]<- time.taken

  #Prediction Time
  start.time <- Sys.time()
  pred = predict(naive_model,X_test)
  end.time <- Sys.time()
  time.taken <- end.time - start.time
  naivebayes_Predictruntime [i]<- time.taken

데이터 세트

전체 코드

balance_data = read.table(file.choose(), sep=",")
attach(balance_data)
x <- balance_data[, c(2,3,4,5)]
y <- balance_data[,1]
X_train <-head(x,500)
Y_train <- head(y,100)
X_test <-tail(x,122)
str(X_train)
str(X_test)
str(Y_train)


decisionTree_Learnruntime = c()
svm_Learnruntime = c()
naivebayes_Learnruntime = c()
knn_Learnruntime = c()

decisionTree_Predictruntime = c()
svm_Predictruntime = c()
naivebayes_Predictruntime =c()
knn_Predictruntime = c()


for (i in 1:20){
  library(e1071)
  library(caret)
  #SVM Model
  start.time <- Sys.time()
  svm_model <- svm(X_train,Y_train)
  end.time <- Sys.time()
  time.taken <- end.time - start.time
  svm_Learnruntime[i]<- time.taken

  #Prediction Time
  start.time <- Sys.time()
  pred <- predict(svm_model,X_test)
  end.time <- Sys.time()
  time.taken <- end.time - start.time
  svm_Predictruntime[i]<- time.taken

  library(rpart)
  #Decision Tree
  #Learn Time
  start.time <- Sys.time()
  tree_model <- rpart(X_train,Y_train)
  end.time <- Sys.time()
  time.taken <- end.time - start.time
  decisionTree_Learnruntime[i]<- time.taken

  #Prediction Time 
  start.time <- Sys.time()
  pred = predict(tree_model,X_test)
  end.time <- Sys.time()
  time.taken <- end.time - start.time
  decisionTree_Predictruntime[i] <- time.taken


  library(e1071)
  #Naive Bayes
  #Learn Time
  start.time <- Sys.time()
  naive_model <-naiveBayes(X_train,Y_train)
  end.time <- Sys.time()
  time.taken <- end.time - start.time
  naivebayes_Learnruntime[i]<- time.taken

  #Prediction Time
  start.time <- Sys.time()
  pred = predict(naive_model,X_test)
  end.time <- Sys.time()
  time.taken <- end.time - start.time
  naivebayes_Predictruntime [i]<- time.taken



}

svm_Learnruntime
svm_Predictruntime
decisionTree_Learnruntime
decisionTree_Predictruntime
naivebayes_Learnruntime
naivebayes_Predictruntime