小编Jam*_*yle的帖子

我想为Caret包创建的模型绘制决策边界.理想情况下,我想从Caret的任何分类器模型的一般案例方法.但是,我目前正在使用kNN方法.我在下面的代码中使用了UCI的葡萄酒质量数据集,这是我现在正在使用的.

我发现这种方法适用于R中的通用kNN方法,但无法弄清楚如何将其映射到Caret - > https://stats.stackexchange.com/questions/21572/how-to-plot-decision-边界的-AK近邻分类器从元素O/21602#21602

    library(caret)

    set.seed(300)

    wine.r <- read.csv('https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv', sep=';')
    wine.w <- read.csv('https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-white.csv', sep=';')

    wine.r$style <- "red"
    wine.w$style <- "white"

    wine <- rbind(wine.r, wine.w)

    wine$style <- as.factor(wine$style)

    formula <- as.formula(quality ~ .)

    dummies <- dummyVars(formula, data = wine)
    dummied <- data.frame(predict(dummies, newdata = wine))
    dummied$quality <- wine$quality

    wine <- dummied

    numCols <- !colnames(wine) %in% c('quality', 'style.red', 'style.white')

    low <- wine$quality <= 6
    high <- wine$quality > 6
    wine$quality[low] = "low"
    wine$quality[high] = "high"
    wine$quality <- as.factor(wine$quality)

    indxTrain <- createDataPartition(y …