Random Forest Regression

Importing the dataset

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

Fitting Random Forest Regression to the dataset

install.packages(‘randomForest’)

library(randomForest)

## randomForest 4.6-14

## Type rfNews() to see new features/changes/bug fixes.

set.seed(1234)
regressor = randomForest(x = dataset[-2],
                         y = dataset$Salary,
                         ntree = 500)

Predicting a new result with Random Forest Regression

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

Visualising the Random Forest Regression results (higher resolution)

install.packages(‘ggplot2’)

library(ggplot2)

## 
## Attaching package: 'ggplot2'

## The following object is masked from 'package:randomForest':
## 
##     margin

x_grid = seq(min(dataset$Level), max(dataset$Level), 0.01)
ggplot() +
  geom_point(aes(x = dataset$Level, y = dataset$Salary),
             colour = 'red') +
  geom_line(aes(x = x_grid, y = predict(regressor, newdata = data.frame(Level = x_grid))),
            colour = 'blue') +
  ggtitle('Truth or Bluff (Random Forest Regression)') +
  xlab('Level') +
  ylab('Salary')