This problem is related to Nearest neighbors classifiers described in section 9.5 in "Modern Statistics with R" - https://modernstatisticswithr.com: Fit a kNN classification model to the wine data, using pH, alcohol, fixed.acidity, and residual.sugar as explanatory variables. Evaluate its performance using 10-fold cross-validation, using AUC to choose the best k.

To solve the problem, you'll need to load the data and libraries with:

# Import data about white and red wines:

white <- read.csv("https://tinyurl.com/winedata1",sep = ";")

red <- read.csv("https://tinyurl.com/winedata2",sep = ";")

# Add a type variable:

white$type <- "white"

red$type <- "red"

# Merge the datasets:

wine <- rbind(white, red)

wine$type <- factor(wine$type)

install.packages('caret', dependencies = TRUE)

library(caret)

# to visualize results you need the following

install.packages('MLeval', dependencies = TRUE)

library(MLeval)

For the submission:

1. 1. Provide the commands in plain text that you used to solve the problem.
2. Attach the figure that resulted after command: plots$roc
3. Output after executed command: plots$optres[[1]][13,]