[R] Support Vector Regression (SVR).ipynb

Kernel: R

Support Vector Regression

Data Preprocessing

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

dataset

Fiting the SVR to the dataset

# install.packages('e1071')
library(e1071)
regressor = svm(formula = Salary ~ ., 
                data = dataset,
                type = 'eps-regression')
summary(regressor)

Call:
svm(formula = Salary ~ ., data = dataset, type = "eps-regression")


Parameters:
   SVM-Type:  eps-regression 
 SVM-Kernel:  radial 
       cost:  1 
      gamma:  1 
    epsilon:  0.1 


Number of Support Vectors:  6

Predicting a new result

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

Visualising the SVR Model results

# install.packages('ggplot2')
library(ggplot2)
ggplot() +
  geom_point(aes(x = dataset$Level, y = dataset$Salary),
             colour = 'red') +
  geom_line(aes(x = dataset$Level, y = predict(regressor, newdata = dataset)),
            colour = 'blue') +
  ggtitle('Truth or Bluff (SVR Model)') +
  xlab('Level') +
  ylab('Salary')

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More continuous visualization

# install.packages('ggplot2')
library(ggplot2)
x_grid = seq(min(dataset$Level), max(dataset$Level), 0.1)
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 (SVR Model)') +
  xlab('Level') +
  ylab('Salary')

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The observation point for CEO is considered as an outlier. This is because the CEO has a much higher Salary then the other Level

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