Decision Tree for Regression in R Programming Last Updated : 28 Jul, 2020 Comments Improve Suggest changes Like Article Like Report Decision tree is a type of algorithm in machine learning that uses decisions as the features to represent the result in the form of a tree-like structure. It is a common tool used to visually represent the decisions made by the algorithm. Decision trees use both classification and regression. Regression trees are used when the dependent variable is continuous whereas the classification tree is used when the dependent variable is categorical. For example, determining/predicting gender is an example of classification, and predicting the mileage of a car based on engine power is an example of regression. In this article, let us discuss the decision tree using regression in R programming with syntax and implementation in R programming. Implementation in R In R programming, rpart() function is present in rpart package. Using the rpart() function, decision trees can be built in R. Syntax: rpart(formula, data, method) Parameters: formula: indicates the formula based on which model has to be fitted data: indicates the dataframe method: indicates the method to create decision tree. "anova" is used for regression and "class" is used as method for classification. Example 1: In this example, let us predict the sepal width using the regression decision tree. Step 1: Install the required package Python3 # Install the package install.packages("rpart") Step 2: Load the package Python3 # Load the package library(rpart) Step 3: Fit the model for decision tree for regression Python3 # Create decision tree using regression fit <- rpart(Sepal.Width ~ Sepal.Length + Petal.Length + Petal.Width + Species, method = "anova", data = iris) Step 4: Plot the tree Python3 # Output to be present as PNG file png(file = "decTreeGFG.png", width = 600, height = 600) # Plot plot(fit, uniform = TRUE, main = "Sepal Width Decision Tree using Regression") text(fit, use.n = TRUE, cex = .7) # Saving the file dev.off() Step 5: Print the decision tree model Python3 # Print model print(fit) Step 6: Predicting the sepal width Python3 # Create test data df <- data.frame (Species = 'versicolor', Sepal.Length = 5.1, Petal.Length = 4.5, Petal.Width = 1.4) # Predicting sepal width # using testing data and model # method anova is used for regression cat("Predicted value:\n") predict(fit, df, method = "anova") Output: n= 150 node), split, n, deviance, yval * denotes terminal node 1) root 150 28.3069300 3.057333 2) Species=versicolor, virginica 100 10.9616000 2.872000 4) Petal.Length=4.05 84 7.3480950 2.945238 10) Petal.Width< 1.95 55 3.4920000 2.860000 20) Sepal.Length=6.35 19 0.6242105 2.963158 * 11) Petal.Width>=1.95 29 2.6986210 3.106897 22) Petal.Length=5.25 22 2.0277270 3.168182 * 3) Species=setosa 50 7.0408000 3.428000 6) Sepal.Length=5.05 22 1.7859090 3.713636 * Predicted value: 1 2.805556 Example 2: In this example, let us predict the mpg value using a decision tree for regression. Step 1: Install the required package Python3 # Install the package install.packages("rpart") Step 2: Load the package Python3 # Load the package library(rpart) Step 3: Fit the model for decision tree for regression Python3 # Create decision tree using regression fit <- rpart(mpg ~ disp + hp + cyl, method = "anova", data = mtcars ) Step 4: Plot the tree Python3 # Output to be present as PNG file png(file = "decTree2GFG.png", width = 600, height = 600) # Plot plot(fit, uniform = TRUE, main = "MPG Decision Tree using Regression") text(fit, use.n = TRUE, cex = .6) # Saving the file dev.off() Step 5: Print the decision tree model Python3 # Print model print(fit) Step 6: Predicting the mpg value using test dataset Python3 # Create test data df <- data.frame (disp = 351, hp = 250, cyl = 8) # Predicting mpg using testing data and model cat("Predicted value:\n") predict(fit, df, method = "anova") Output: n= 32 node), split, n, deviance, yval * denotes terminal node 1) root 32 1126.04700 20.09062 2) cyl>=5 21 198.47240 16.64762 4) hp>=192.5 7 28.82857 13.41429 * 5) hp< 192.5 14 59.87214 18.26429 * 3) cyl< 5 11 203.38550 26.66364 * Predicted value: 1 13.41429 Advantages of Decision Trees Considers all the possible decisions: Decision trees considers all the possible decisions to create a result of the problem. Easy to use: With classification and regression techniques, it is easy to use for any type of problem and further creating predictions and solving the problem. No problem with missing values: There is no problem with the datasets having missing values and do not affect the decision tree building. Disadvantages of Decision Trees Requires higher time: Decision trees requires higher time for the calculation for large datasets. Less learning: Decision trees are not good learners. Random forest approach is used for better learning. Comment More infoAdvertise with us Next Article Decision Tree Classifiers in R Programming U utkarsh_kumar Follow Improve Article Tags : R Language R Machine-Learning R regression R Data-science Similar Reads Machine Learning with R Machine Learning as the name suggests is the field of study that allows computers to learn and take decisions on their own i.e. without being explicitly programmed. 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