ggplot2中的决策边界图

Question

我一直在研究Rafael A. Irizarry的书Introduction to data science，并且不断遇到我想重新创建的决策边界图（下图中的左图）

我找到了一个代码，可以在https://michael.hahsler.net/SMU/EMIS7332/R/viz_classifier.html上创建决策边界图，但是可以完成工作，但是这些图看起来并不像书中的那样。

library(randomForest)
library(tidyverse)
library(caret)
library(dslabs)

decisionplot <- function(model, data, class = NULL, predict_type = "class",
                         resolution = 100, showgrid = TRUE, ...) {

  if(!is.null(class)) cl <- data[,class] else cl <- 1
  data <- data[,1:2]
  k <- length(unique(cl))

  plot(data, col = as.integer(cl)+1L, pch = as.integer(cl)+1L, ...)

  # make grid
  r <- sapply(data, range, na.rm = TRUE)
  xs <- seq(r[1,1], r[2,1], length.out = resolution)
  ys <- seq(r[1,2], r[2,2], length.out = resolution)
  g <- cbind(rep(xs, each=resolution), rep(ys, time = resolution))
  colnames(g) <- colnames(r)
  g <- as.data.frame(g)

  ### guess how to get class labels from predict
  ### (unfortunately not very consistent between models)
  p <- predict(model, g, type = predict_type)
  if(is.list(p)) p <- p$class
  p <- as.factor(p)

  if(showgrid) points(g, col = as.integer(p)+1L, pch = ".")

  z <- matrix(as.integer(p), nrow = resolution, byrow = TRUE)
  contour(xs, ys, z, add = TRUE, drawlabels = FALSE,
          lwd = 2, levels = (1:(k-1))+.5)

  invisible(z)
}


train_rf<- randomForest(y~., data = mnist_27$train)

decisionplot(train_rf, data= mnist_27$train %>% select(x_1, x_2, y) , class="y")

我需要帮助来像书中那样绘制决策边界图。

Answer 1

感谢尼尔森。看到您的链接和其他一些资源，就可以了。

library(randomForest)
library(tidyverse)
library(caret)
library(dslabs)
library(ggthemes)

model<- randomForest(y~., data = mnist_27$train)
data<- mnist_27$train %>% select(x_1, x_2, y)
class<- "y"
#predict_type = "class"
resolution = 75


  if(!is.null(class)) cl <- data[,class] else cl <- 1
  data <- data[,1:2]




  r <- sapply(data, range, na.rm = TRUE)
  xs <- seq(r[1,1], r[2,1], length.out = resolution)
  ys <- seq(r[1,2], r[2,2], length.out = resolution)
  g <- cbind(rep(xs, each=resolution), rep(ys, time = resolution))
  colnames(g) <- colnames(r)
  g <- as.data.frame(g)

  q<- predict(model, g, type = "class")
  p <- predict(model, g, type = "prob")
  p<- p %>% as.data.frame() %>% mutate(p=if_else(`2`>=`7`, `2`, `7`))
  p<- p %>% mutate(pred= as.integer(q))







  ggplot()+
    geom_raster(data= g, aes(x= x_1, y=x_2, fill=p$`2` ), interpolate = TRUE)+
    geom_contour(data= NULL, aes(x= g$x_1, y=g$x_2, z= p$pred), breaks=c(1.5), color="black", size=1)+
    theme_few()+
    scale_colour_manual(values = cols)+
    labs(colour = "", fill="")+
    scale_fill_gradient2(low="#338cea", mid="white", high="#dd7e7e", 
                         midpoint=0.5, limits=range(p$`2`))+
    theme(legend.position = "none")