This problem is related to Nearest neighbors classifiers described in section 9.5 in “Modern Statistics with R” – https://modernstatisticswithr.
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. Provide the commands in plain text that you used to solve the problem.
Attach the figure that resulted after command: plots$roc
Output after executed command: plots$optres[[1]][13,]
Attach the figure that resulted after command: plots$cc
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