test.R

        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 = wine[, names(wine) == "quality"], p = 0.7, list = F)

        train <- wine[indxTrain,]
        test <- wine[-indxTrain,]

        corrMat <- cor(train[, numCols])
        correlated <- findCorrelation(corrMat, cutoff = 0.6)

        ctrl <- trainControl(
                             method="repeatedcv",
                             repeats=5,
                             number=10,
                             classProbs = T
                             )

        t1 <- train[, -correlated]
        grid <- expand.grid(.k = c(1:20))

        knnModel <- train(formula, 
                          data = t1, 
                          method = 'knn', 
                          trControl = ctrl, 
                          tuneGrid = grid, 
                          preProcess = 'range'
                          )

        t2 <- test[, -correlated]
        knnPred <- predict(knnModel, newdata = t2)

        # How do I render the decision boundary?
        
        # Make knnPred numeric
        knnPredNumeric <- as.numeric(sample(knnPred, 100))

        # choose X, Y axis -> We'll plot by Alcohol & Total Sulfur Dioxide features,
        al <- seq(min(test$alcohol), max(test$alcohol), by=0.1)

        chl <- seq(min(test$chlorides), max(test$chlorides), by=.001)

        probs <- matrix(knnPredNumeric, length(al), length(chl))

        contour(al, chl, probs, labels="", xlab="", ylab="", main="X-Nearest Neighbor", axes=F)

        gd <- expand.grid(x=al, y=chl)

        points(gd, pch=".", cex=5, col=ifelse(probs==1, "coral", "cornflowerblue"))
        box()