Decision Tree in R: Components, Types, Steps to Build, Challenges

“Decision tree in R” is the graphical representation of choices that can be made and what their results might be. It is represented in the form of a graphical tree. Different parts of the tree represent various activities of the decision-maker. It is an efficient way of visually laying down the different possibilities and outcomes of a particular action.

Why should I use a Decision Tree in R?

You might question the importance of decision trees in R. Not only do decision trees lay out the problem and different solutions but also all the possible options. These options can be the challenges faced by the decision-maker to come up with a broader range of solutions.

It also helps analyze the different possible consequences of a problem and plan in advance. It gives a comprehensive framework so you can easily quantify the values of different outcomes also. This is particularly important when conditional probability comes into the picture.

Applications of Decision Trees

Decision trees are applied in the following fields:

Sales and Marketing – Decision trees are crucial in a decision-oriented industry like marketing. Specific organizations utilize decision tree regression to take deliberate action after understanding the effects of marketing activity. Decision trees help to break down large amounts of data sets into smaller subsets, making effective judgments that increase earnings and reduce losses.

Fraud and Anomaly Detection– Financial sectors are particularly vulnerable to fraud. These businesses use decision trees to give them the information they need to identify fraudulent consumers and filter out abnormal or fraudulent loan applications, information, and insurance deals.

Health Diagnosis– Classification trees help doctors identify people at risk of developing major illnesses like diabetes and cancer.

Low churn rate- Banks utilize decision tree regression in machine learning algorithms to keep their clients. Since keeping consumers is usually less expensive than finding new ones, analyzing which consumers are most likely to stop doing business with a bank can be profitable. Authorities can make judgments based on the results and respond by offering improved services, discounts, and a variety of other features. Ultimately, this lowers the churn rate.

Options in a decision tree

  • Maximum Depth- This specifies how many depth levels a tree may be shaped at.
  • Minimum Number of Records in Terminal Nodes – This is important for figuring out how many records a terminal node will accept at the most. The split is not implemented if it lowers the results below the predetermined level.
  • Differentiated Clusters Output
  • The minimal number of records in the parent node is comparable to the minimum number of records in the terminal nodes we previously mentioned. The application where a split takes place is where the distinction resides. The split procedure is terminated if the number of records is much fewer than provided.
  • When the chi-square statistic for a categorical input is compared with the target test, modifications are made using the Bonferroni correction.

What are the different parts of a decision tree in R?

To understand and interpret what a decision tree means, you have to understand what the different parts of a decision tree are. You might come across these terms very often when you look at decision trees.

  • Nodes: The nodes of a tree represent an event that has taken place or a choice that the decision-maker has to make.
  • Edges: These are the different conditions or rules that are set.
  • Root Node: This shows the entire population or sample in case of a visualization of a sample.
  • Splitting: This is when the node is divided into sub-nodes.
  • Decision nodes: These are the specific sub-nodes that split further.
  • Leaf: These are the end-terms or the nodes that do not split also.
  • Pruning: This is the removal of sub-nodes of a decision node.
  • Branch: These are sub-sections of an entire decision tree.

Read: Data Science vs Decision Science

How can I use the decision tree in R?

Since decision trees can only be made in R, you need to install R first. This can be done very quickly online. After you download R, you have to create and visualize packages to use decision trees. One package that allows this is “party”. When you type in the command install.package (“party”), you can use decision tree representations. Decision trees are also considered to be complicated and supervised algorithms.

How do decision trees work in R?

Decision trees are more often used in machine learning and data mining when you are using R. The essential element used in this case is the observed or training data. After this, a comprehensive model is created. A set of validation data is also used to upgrade and improve the decision tree.

Learn more: Data Visualization in R programming

What are the different types of decision trees?

The most important types of decision trees are the Classification and Regression Trees. These are generally used when the inputs and outputs are categorical. 

Classification Trees: These are tree models where the variable can take a specific set of values. In these cases, the leaves represent the class labels, while the branches represent the conjunctions of a different feature. It is generally a “yes” or “no” type of tree.

Regression Trees: There are decision trees that have a variable which can take continuous values.

When you combine both the above type of decision trees, you get the CART or classification and regression trees. This is an umbrella term, which you might come across several times. These refer to the above-mentioned procedures. The only difference in these two is the type of dependent variables – either categorical or numeric. 

Instructions for Creating R Decision Trees

Decision Trees help to create recursive partitioning algorithms. The following are the steps to follow for creating decision tree algorithms:

  • First, the best strategy for data splitting should be evaluated quantitatively for each input variable.
  • The optimal split should be chosen, and then the data should be divided into subgroups following the split’s structure.
  • After choosing a subgroup, we repeat step 1 for each of the underlying subgroups.
  • Once the split corresponding to the same target variable value is reached, the splitting must continue until it stops.

What are the steps involved in building a decision tree on R?

Step 1: Import- Import the data set that you want to analyze.

Step 2: Cleaning- The data set has to be cleaned.

Step 3: Create a train or test set- This implies that the algorithm has to be trained to predict the labels and then used for inference.
Step 4: Build the model- The syntax rpart() is used for this. This means that the nodes keep splitting till a point is reached wherein further splitting is not possible.

Step 5: Predict your dataset- Use the syntax predict() for this step.

Step 6: Measure performance- This step shows the accuracy of the matrix. 

Step 7: Tune the hyper-parameters- To control the aspects of the fit, the decision tree has various parameters. The parameters can be controlled using the rpart.control() function.

Frequently Used R Decision Tree Algorithms

The three most typical Decision Tree Algorithms are as follows:

  1. CART (Classification and Regression Tree) examines a wide range of factors.
  2. The goal of Zero (created by J.R. Quinlan) is to maximize the knowledge gained by assigning each person to a branch of the tree.
  3. Chi-Square Automation Interaction Detection (CHAID) is used to investigate discrete, qualitative, independent, and dependent variables.

Also Read: R Tutorial for Beginners

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What are the challenges of using a decision tree in R?

Pruning can be a tedious process and needs to be done carefully to get an accurate representation. There can also be high instability in case of even a small change. So, it is highly volatile, which can be troublesome for users, especially beginners. Moreover, it can fail to produce desirable outcomes and results in a few cases. 

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Wrapping up

If you want to make an optimal choice while also being aware of what the consequences will be, make sure you know how to use the decision tree in R. It is a schematic representation of what might happen and what might not. There are several different components of a decision tree, which are explained above. It is a popular and powerful machine-learning algorithm to use.

What is a decision tree and its categories?

A decision tree is a supporting tool that possesses a tree-like structure for modeling probable outcomes, possible consequences, utilities, and also the cost of resources. Decision trees make it easy to display different algorithms with the help of conditional control statements. A decision tree includes branches for representing different decision-making steps that eventually lead to a favorable result.

Based on the target variable, there are two main types of decision trees.

1. Categorical Variable Decision Tree - In this decision tree, the target variables are divided into different categories. The categories will determine that every decision process will fall into either category, and there are no chances of in-betweens in any case.
2. Continuous Variable Decision Tree - There is a continuous target variable in this decision tree. For instance, if the income of any individual is unknown, then it could be known with the help of available information like age, occupation, and any other continuous variable.

What are the applications of decision trees?

There are two main applications of decision trees.

1. Using demographic data for finding prospective clients - Any organization can streamline its marketing budget for making informed decisions so that the money is spent at the right place with proper demographic data in mind.
2. Assessing prospective growth opportunities - Decision trees are helpful in evaluating the historical data for assessing the prospective growth opportunities in any business and help with expansion.

What are the pros and cons of decision trees?


1. Easy to read and interpret - You can easily read and interpret the outputs of decision trees even without any statistical knowledge.
2. Easy to prepare - Decision trees require very little effort for data preparation as compared to any other decision technique.
3. Less requirement of data cleaning - Decision trees require pretty little data cleaning as the variables are already created.


1. Unstable nature - The biggest limitation is that decision trees are highly unstable as compared to other decision techniques. Even if there is a small change in the data, it will reflect a huge change in the decision structure.
2. Less effective for predicting the outcomes of a continuous variable - When variables have to be categorized into several categories, decision trees tend to lose information.

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