分层k折叠情况下如何计算混淆矩阵之和

Question

我是机器学习的新手。我在数据集上应用了分层k折。如何获得混淆矩阵的和。

下面是我的代码

library(MASS)

cv_lda <- lapply(folds, function(x) { # start of function
  # in the next two lines we will separate the Training set into it's 10 pieces
  training_fold = ForwardPlayers[-x, ] # training fold =  training set minus (-) it's sub test fold
  test_fold = ForwardPlayers[x, ] # here we describe the test fold individually
  # now apply (train) the classifer on the training_fold
  classifier = lda(Rating ~ .,training_fold)
  # next step in the loop, we calculate the predictions and cm and we equate the accuracy
  # note we are training on training_fold and testing its accuracy on the test_fold
  y_pred = predict(classifier, newdata = test_fold[-1]) 

  cm = table(test_fold$Rating, y_pred$class)
  #accuracy <- sum(diag(cm))/sum(cm)

  return(cm)
})

Answer 1

您尝试过吗？

addmargins（）

->或任何保证金子参数

作为论点？