my_knn_cv: k-Nearest Neighbour Classification

In txqtiffany/STAT302package: Package Building Demonstration

Description

k-nearest neighbour classification for test set from training set. For each row of the test set, the k nearest (in Euclidean distance) training set vectors are found, and the classification is decided by majority vote, with ties broken at random. If there are ties for the kth nearest vector, all candidates are included in the vote.

Usage

my_knn_cv(train, cl, k_nn, k_cv)

Arguments

train	input data frame
cl	true class value of your training data
k_nn	integer representing the number of neighbors
k_cv	integer representing the number of folds

Value

a list with objects

class	a vector of the predicted class Ŷ i for all observations
cv_err	a numeric with the cross-validation misclassification error

Examples

library(palmerpenguins)
data(package = "palmerpenguins")
penguins_df <- na.omit(penguins)
train <- lapply(
  penguins_df[c("bill_length_mm", "bill_depth_mm", "flipper_length_mm", "body_mass_g")],
   as.numeric)
cl <- as.numeric(penguins_df$species)
nearest_1 <- my_knn_cv(train, cl, 1, 5)
nearest_5 <- my_knn_cv(train, cl, 5, 5)

txqtiffany/STAT302package documentation built on Dec. 23, 2021, 1:03 p.m.