使用R获取KNN分类器的决策边界

Question

我正在尝试拟合KNN模型并使用R中的ISLR包中的Auto数据集获取决策边界。

在这里，我很难确定3类问题的决策边界。到目前为止，这是我的代码。我无法确定决策边界。

我在该网站的其他地方看到了使用ggplot解决此类问题的答案。但是我想使用plot函数以经典方式获得答案。

 library("ISLR")

trainxx=Auto[,c(1,3)]
trainyy=(Auto[,8])

n.grid1 <- 50

x1.grid1 <- seq(f = min(trainxx[, 1]), t = max(trainxx[, 1]), l = n.grid1)
x2.grid1 <- seq(f = min(trainxx[, 2]), t = max(trainxx[, 2]), l = n.grid1)
grid <- expand.grid(x1.grid1, x2.grid1)

library("class")
mod.opt <- knn(trainxx, grid, trainyy, k = 10, prob = T)

prob_knn <- attr(mod.opt, "prob") 

我的问题主要是在此代码段之后。我非常确定我必须修改以下部分。但是我不知道如何。我是否需要在此处使用“嵌套的条件”？

prob_knn <- ifelse(mod.opt == "3", prob_knn, 1 - prob_knn) 



prob_knn <- matrix(prob_knn, n.grid1, n.grid1)


plot(trainxx, col = ifelse(trainyy == "3", "green",ifelse(trainyy=="2", "red","blue")))
title(main = "plot of training data with Desicion boundary K=80")
contour(x1.grid1, x2.grid1, prob_knn, levels = 0.5, labels = "", xlab = "", ylab = "", 
        main = "", add = T , pch=20)

如果有人可以提出解决此问题的建议，将会有很大的帮助。

基本上我需要这样的东西来解决3类问题 https://stats.stackexchange.com/questions/21572/how-to-plot-decision-boundary-of-ak-nearest-neighbor-classifier-from-elements-o

Answer 1

这是一种调整后的方法，将决策边界绘制为线。我认为这需要每个类别的预测概率，但在阅读此答案后，结果表明您可以将每个类别的预测概率标记为 1，无论该类别是预测的，否则为零。

# Create matrices for each class where p = 1 for any point
#   where that class was predicted, 0 otherwise
n_classes = 3
class_regions = lapply(1:n_classes, function(class_num) {
    indicator = ifelse(mod.opt == class_num, 1, 0)
    mat = matrix(indicator, n.grid1, n.grid1)
})

# Set up colours
class_colors = c("#4E79A7", "#F28E2B", "#E15759")
# Add some transparency to make the fill colours less bright
fill_colors = paste0(class_colors, "60")

# Use image to plot the predicted class at each point
classes = matrix(as.numeric(mod.opt), n.grid1, n.grid1)
image(x1.grid1, x2.grid1, classes, col = fill_colors, 
      main = "plot of training data with decision boundary",
      xlab = colnames(trainxx)[1], ylab = colnames(trainxx)[2])
# Draw contours separately for each class
lapply(1:n_classes, function(class_num) {
    contour(x1.grid1, x2.grid1, class_regions[[class_num]], 
            col = class_colors[class_num],
            nlevels = TRUE, add = TRUE, lwd = 2, drawlabels = FALSE)
})
# Using pch = 21 for bordered points that stand out a bit better
points(trainxx, bg = class_colors[trainyy], 
       col = "black",
       pch = 21)

结果图：