AI4OPT - Lecture 7 - DE&M II - Using RMSE

# Load package
library(caret)

## Warning: package 'caret' was built under R version 4.1.3

## Loading required package: ggplot2

## Loading required package: lattice

library(mlbench)

## Warning: package 'mlbench' was built under R version 4.1.3

# Load the dataset
data(PimaIndiansDiabetes)

# prepare resampling method

trainControl <- trainControl(method ="cv", number=5)
set.seed(7)
fit <- train(diabetes~., data=PimaIndiansDiabetes, method="glm", metric = "Accuracy", trControl = trainControl)

# display results
print(fit)

## Generalized Linear Model 
## 
## 768 samples
##   8 predictor
##   2 classes: 'neg', 'pos' 
## 
## No pre-processing
## Resampling: Cross-Validated (5 fold) 
## Summary of sample sizes: 614, 614, 615, 614, 615 
## Resampling results:
## 
##   Accuracy   Kappa   
##   0.7799168  0.490744