Decision Tree Regression:
Decision tree builds regression or classification models in the form of a tree structure.
It breaks down a datasets into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed.
The final result is a tree with decision nodes and leaf nodes. A decision node (e.g., Outlook) has two or more branches (e.g., Sunny, Overcast and Rainy), each representing values for the attribute tested.
Leaf node (e.g., Hours Played) represents a decision on the numerical target.
The topmost decision node in a tree which corresponds to the best predictor called root node.
Decision trees can handle both categorical and numerical data.
Project | 05
Project | 05 Decision Tree Regression
# Importing the dataset
dataset = read.csv('Position_Salaries.csv')
dataset = dataset[2:3]
# Splitting the dataset into the Training set and Test set
# # install.packages('caTools')
# library(caTools)
# set.seed(123)
# split = sample.split(dataset$Salary, SplitRatio = 2/3)
# training_set = subset(dataset, split == TRUE)
# test_set = subset(dataset, split == FALSE)
# Feature Scaling
# training_set = scale(training_set)
# test_set = scale(test_set)
# Fitting Decision Tree Regression to the dataset
# install.packages('rpart')
library(rpart)
regressor = rpart(formula = Salary ~ .,
data = dataset,
control = rpart.control(minsplit = 1))
# Predicting a new result with Decision Tree Regression
y_pred = predict(regressor, data.frame(Level = 6.5))
# Visualising the Decision Tree Regression results (higher resolution)
# install.packages('ggplot2')
library(ggplot2)
x_grid = seq(min(dataset$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 (Decision Tree Regression)') +
xlab('Level') +
ylab('Salary')
# Plotting the tree
plot(regressor)
text(regressor)