Decision Tree

Importing the dataset

dataset = read.csv('C:/RClass/Position_Salaries.csv')
dataset = dataset[2:3]

Fitting Decision Tree Regression to the dataset

# install.packages('rpart')
library(rpart)
regressor = rpart(formula = Salary ~.,
                  data = dataset,
                  control = rpart.control(minsplit = 1))

Predicting a new result

y_pred = predict(regressor, data.frame(Level = 6.5))

Visualizaing the Decision Tree Regression Results

library(ggplot2)

## Warning: package 'ggplot2' was built under R version 4.1.3

ggplot() + 
  geom_point(aes(x=dataset$Level, y=dataset$Salary), 
             color='red') +
  geom_line(aes(x=dataset$Level, y=predict(regressor, newdata=dataset)), 
            color='blue') +
  ggtitle('Truth or Bluff (Decision Tree Regression)') +
  xlab('Level') + ylab('Salary')

Visualizaing the Decision Tree Regression Results (for higher resolution and smoother curve)

library(ggplot2)
x_grid = seq(min(dataset$Level), max(dataset$Level), 0.1)
ggplot() + 
  geom_point(aes(x=dataset$Level, y=dataset$Salary), 
             color='red') +
  geom_line(aes(x=x_grid, y=predict(regressor, newdata=data.frame(Level=x_grid))), 
            color='blue') +
  ggtitle('Truth or Bluff (Decision Tree Regression)') +
  xlab('Level') + ylab('Salary')