Explain with a case study of implementation of decision tree regression in R

478    Asked by AishwaryaJhadav in Data Science , Asked on Nov 9, 2019
Answered by Aishwarya Jhadav

Initially we import and split the data

# 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)

Now we will fit and predict the data

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))



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