How to Make a Decision Tree?

A decision tree is a graphical representation you can use to visualize how to solve a problem by mapping out your sequence of decisions and possible outcomes. Starting first with the main decision you want to make, the “root” node, you add “branches,” leading to the possible “decisions” regarding how to approach the “root.” You then add more “branches” connecting each “decision” node to its potential outcome, called the “leaf” nodes.

It’s a beneficial tool for both business and personal decision-making processes. It allows you to visualize outcomes clearly and assess the potential impact of different choices. A decision tree can be as simple as a drawn diagram or more complex using algorithms. 

Explore how to make a decision tree, understand its various types, and discover its applications in real-world scenarios. 

Decision tree flowchart options

In its simplest form, a decision tree is a flowchart diagram in the shape of a tree, mapping possible decisions; however, you’ll find two different types of decision trees. Understanding these types helps select the right tree for your decision-making scenario.

Classification trees: Use this type of decision tree for decisions that result in categorical outcomes, such as if you are looking for yes or no answers to your questions. For example, “Has the business sold every product?” 

Regression trees: Use this model when the outcome is a continuous value you can calculate using historical data. An example might be, “How many products will the company sell this holiday?”

What is the purpose of decision trees?

Decision trees are a versatile tool that you can employ for multiple purposes ranging from improved decision-making to enhanced risk assessment. Professionals use them in all business areas, including strategic planning, operations, research, risk analysis, and project management. You can use one to assist you in decision-making for various reasons. Some examples include:

• Simplifying decision-making: A decision tree can help you make decisions by breaking down complex choices into manageable parts or deciding between more than one course of action. 

• Risk assessment: Risk analysts and insurance professionals use decision trees to calculate risks in decision-making. With a diagram, it’s easier to calculate and see the level of risk for each decision option, to determine which risks are worth taking, and to perceive which ones are too volatile.

• Cost-benefit analysis: Finance professionals use decision trees to determine the potential results of a financial decision, which allows them to choose the decision with the best possible outcome. 

How to make a decision tree

You can draw a decision tree manually on paper or with the aid of a template or software tools like Microsoft Excel, Lucidchart, or Slickplan. Check out the steps below that outline the process to help you begin.

1. Identify the main decision or problem: The main decision you want to make or the question you wish to answer becomes the root of the tree, or the “root node.” Write this in a box with plenty of space around it.

2. Add your decisions: Starting from the root node, draw lines to the right like branches, representing possible decisions you might make. Label each of these branches. These labels display your decision nodes. You can also add chance nodes that look like alternate branches, representing multiple possible outcomes for each decision.

3. Expand to endpoints: When every branch has an endpoint or endpoint node, you have no more outcomes to explore.

4. Add values: With all your endpoints in place, you can add any numbers and values to calculate each decision's risk. Usually, these are monetary values, such as the cost to develop a product or app,  allowing for a clear comparison of different courses of action based on their potential outcomes.

5. Analyze your tree: Now your decision tree is complete, you can analyze it and decide which decision to take. Remember that when analyzing your tree, you don’t always choose the outcome with the highest possible value because it may also come with the largest risk.

Who uses a decision tree?

Professionals across many industries leverage decision trees as their use is so versatile. You can use decision trees for strategic planning, cost analysis, and other risk management needs, to name a few functions. Industries that make use of this tool include:

• Finance

• Education

• Health care

• Law

• Business 

• Marketing 

• Retail

Pros and cons of using a decision tree

Decision trees are a proven decision-making tool, with professionals in a variety of industries using them for a range of problems. Still, as with any methodology or technology, decision trees come with several advantages and disadvantages that you must weigh to decide whether it is a tool you wish to use. 

Advantages

Decision trees help visualize the decision-making process while offering flexibility without needing lots of resources. Advantages include:

• Clear and collaborative: A decision tree is an effective way to map out a decision-making process that the whole team can take part in. A visual like this is accessible for all and gives a clear picture of what needs to happen going forward.

• Flexibility in application: Decision tree diagrams are very flexible. Each decision leads to a new outcome, and you can adapt these as necessary, as well as the numerical data you input. You can also use a decision tree for regressions or classification, making them versatile.

• Efficient: You can build a decision tree with very few resources and in little time. Decision tree algorithms can convert data into categorical values, making them highly efficient and flexible.

Disadvantages 

While you’ll find many advantages to using a decision tree, it can also present challenges, such as issues with complexity and accuracy. Potential drawbacks include:

• Complexity: While decision trees create a simple way to visualize a decision-making process, they can become complicated when you add too many branches. For your decision tree to be as successful as possible, try to keep it simple.

• Accuracy: Unless your data is accurate, it can skew decisions. Decision tree algorithms may produce a different outcome with only slight variations in the figures provided.

How to start making a decision tree

Creating a decision tree is available to all in its simplest form, such as drawing it by hand. You can also utilize decision-tree software or consider using a template. 

Gain proficiency in decision tree software like Lucidchart or develop your understanding of machine learning for more complex analyses, such as regression and classification, with a course like Supervised Machine Learning: Regression and Classification. Practice with real-life scenarios and apply what you've learned by creating decision trees for real or hypothetical decision-making situations.

Continue learning about decision tree analysis on Coursera

A decision tree flowchart visually represents a decision you want to make. You can create a simple version on paper or use templates and software for more complex versions. To learn more about decision trees and the relevant algorithms, you might consider diving deeper into data analytics witht the Google Data Analytics Professional Certificate on Coursera where you’ll learn in-demand skills, and get AI training from Google experts.