Question#2

2. Carefully explain the differences between the KNN classifier and KNN

library(knitr)
library(kableExtra)

# Create a data frame
knn_table <- data.frame(
  "KNN classifier" = c("Response variable is categorical", 
                       "Uses majority vote among its nearest K neighbors to predict class for new observation",
                       "Neighbors vote their own class, and the most common is assigned as the predicted label"),
  
  "KNN regression" = c("Response variable is continuous", 
                       "The predicted value is computed as the weighted average of the K nearest neighbors response value",
                       "Closer neighbors contribute more to the prediction if weights are used")
)

# Generate the table
kable(knn_table, align = "l", booktabs = TRUE, escape = FALSE) %>%
  kable_styling(full_width = FALSE)

KNN.classifier	KNN.regression
Response variable is categorical	Response variable is continuous
Uses majority vote among its nearest K neighbors to predict class for new observation	The predicted value is computed as the weighted average of the K nearest neighbors response value
Neighbors vote their own class, and the most common is assigned as the predicted label	Closer neighbors contribute more to the prediction if weights are used