尝试使用 R 中 k 的最佳值从交叉验证结果创建混淆矩阵

Question

我已经使用 R 中的鸢尾花数据集编写了下面的 knn 交叉验证方法。我如何从中获得最佳的 k 值并基于此创建一个混淆矩阵？任何帮助都会很棒。

library(class)
data("iris")
kfolds = 5
iris$folds = cut(seq(1,nrow(iris)),breaks=kfolds,labels=FALSE)
iris$folds

# Sets the columns to use as predicators
pred = c("Petal.Width", "Petal.Length")
accuracies = c()
ks = c(1,3,5,7,9,11,13,15)
for (k in ks) {
  k.accuracies = c()
  for(i in 1:kfolds) {
    # Builds the training set and test set for this fold.
    train.items.this.fold  = iris[iris$folds != i,] 
    validation.items.this.fold = iris[iris$folds == i,]

    # Fit knn model on this fold.
    predictions = knn(train.items.this.fold[,pred], 
                      validation.items.this.fold[,pred], 
                      train.items.this.fold$Species, k=k)

    predictions.table <- table(predictions, validation.items.this.fold$Species)

    # Work out the amount of correct and incorrect predictions.
    correct.list <- predictions == validation.items.this.fold$Species
    nr.correct = nrow(validation.items.this.fold[correct.list,])

    # Get accuracy rate of cv.
    accuracy.rate = nr.correct/nrow(validation.items.this.fold)

    # Adds the accuracy list.
    k.accuracies <- cbind(k.accuracies, accuracy.rate)
  }
  # Adds the mean accuracy to the total accuracy list.
  accuracies <- cbind(accuracies, mean(k.accuracies))
}

# Accuracy for each value of k: visualisation.
accuracies

更新：

predictions.table <- table(predictions == ks[which.max(accuracies)], validation.items.this.fold$Species)

Answer 1

你的代码有一些问题，这个运行：

library(class)
data("iris")
kfolds = 5
iris$folds = cut(seq(1,nrow(iris)),breaks=kfolds,labels=FALSE)
iris$folds

# Sets the columns to use as predicators
pred = c("Petal.Width", "Petal.Length")
accuracies = c()
ks = c(1,3,5,7,9,11,13,15)
k.accuracies = c()
predictions.list = list()
for (k in ks) {
  k.accuracies = c()
  for(i in 1:kfolds) {
    # Builds the training set and test set for this fold.
    train.items.this.fold  = iris[iris$folds != i,] 
    validation.items.this.fold = iris[iris$folds == i,]

    # Fit knn model on this fold.
    predictions = knn(train.items.this.fold[,pred], 
                      validation.items.this.fold[,pred], 
                      train.items.this.fold$Species, k=k)
    predictions.list[[i]] = predictions

    predictions.table <- table(predictions, validation.items.this.fold$Species)

    # Work out the amount of correct and incorrect predictions.
    correct.list <- predictions == validation.items.this.fold$Species
    nr.correct = nrow(validation.items.this.fold[correct.list,])

    # Get accuracy rate of cv.
    accuracy.rate = nr.correct/nrow(validation.items.this.fold)

    # Adds the accuracy list.
    k.accuracies <- cbind(k.accuracies, accuracy.rate)
  }
  # Adds the mean accuracy to the total accuracy list.
  accuracies <- cbind(accuracies, mean(k.accuracies))
}
accuracies


predictions.table <- table(predictions.list[[which.max(accuracies)]], validation.items.this.fold$Species)

当你调用predictions.table <- table(predictions, validation.items.this.fold$Species)时，这是混淆矩阵，你是用准确率作为评估指标，所以最好的K就是最好的准确率。你可以这样得到最好的K值：

ks[which.max(accuracies)]

更新

创建一个列表来存储每个预测，然后使用最佳准确度创建混淆矩阵。

尝试使用 R 中 k 的最佳值从交叉验证结果创建混淆矩阵

Trying to create confusion matrix from cross-validated results using the best value of k in R

r

cross-validation

knn

confusion-matrix