r - 使用 R 获取 KNN 分类器的决策边界
问题描述
我正在尝试使用 R 中 ISLR 包中的 Auto 数据集来拟合 KNN 模型并获得决策边界。
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。我没有得到决策边界。
我在这个网站的其他地方看到了使用 ggplot 的这类问题的答案。但我想使用绘图功能以经典方式得到答案。
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")
我的问题主要是在这个代码段之后。我很确定我必须修改以下部分。但我不知道怎么做。我需要在这里使用“嵌套如果”吗?
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,那将是一个很大的帮助。
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
解决方案
这是一种将决策边界绘制为线条的调整方法。我认为这需要每个类的预测概率,但在阅读了这个答案之后,你可以将每个类的预测概率标记为 1,无论该类被预测到哪里,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: