train linear regression in R using kfold method

kfold_regression.R

## R version 3.6.1
## Created by DataRockie 15 November 2019

## load library
library(caret)
library(mlbench)
library(dplyr)

## load dataset
data("BostonHousing")

## tibble dataframe
BostonHousing <- as.tbl(BostonHousing)

## create folds
(folds <- createFolds(BostonHousing$medv, k=5, list=T))

## train linear regression
kfoldLM <- function(data, k) {
  folds <- createFolds(BostonHousing$medv, k=k, list=T)
  result <- vector()
  for(fold in folds) {
    trainData <- data[-fold, ]
    testData <- data[fold, ]
    r2 <- summary(lm(trainData))$r.squared
    result <- append(result, r2) 
  }
  cat("Average R2:", round(mean(result),4) )
  cat("\nStandard Deviation R2:", round(sd(result),4) )
}

## test function with k=5
kfoldLM(data = BostonHousing, k = 5)