Classify with kknn method with and without cross-validation

basicClassification.R

library(kknn)
library(dplyr)
library(stringr)

set.seed(2342)

# Data preprocessing

data <- 
  read.csv('lab8_data.csv', sep=',') %>% 
  mutate(Diagnosis = str_replace_all(Diagnosis, " ", "")) %>% 
  mutate(Diagnosis = factor(Diagnosis)) %>% 
  mutate(Sex = factor(Sex)) %>% 
  mutate(Left_right = factor(Left_right)) %>% 
  select(-contains(".mm")) %>% 
  select(-X) %>% 
  mutate(Sex = as.numeric(Sex)) %>% 
  mutate(Left_right = as.numeric(Left_right)) 

learn_to_test <- 5/1

observations_count <- dim(data)[1]
learn_idx <- sample(seq_len(observations_count),
                    observations_count*learn_to_test/(learn_to_test+1))
learn_data <- data[learn_idx, ]
test_data <- data[-learn_idx, ]

# Computations

kknn(Diagnosis ~ .,
     learn_data,
     test_data,
     kernel = "triangular",
     k = 7,
     distance = 1) %>% 
  summary

train.kknn(Diagnosis ~ .,
           data,
           kmax = 16,
           kernel = c("rectangular", "triangular", "epanechnikov"),
           distance = 2) %>% 
  plot(.,
       main = paste("Kmeans clusterisation\n",
                    "Best method:", .$best.parameters$kernel, ".",
                    "Best k:", .$best.parameters$k, "\n"))