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What is a decision tree, and how do you create one?

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Making good decisions is tough, especially when you have multiple options and uncertain outcomes. Decision trees give you a clear way to map out choices and their potential consequences, helping you make smarter decisions with confidence. 

In this article, we’ll discuss what decision trees are, how they work, and how to create your own. Whether you’re analyzing data or just trying to make a complex business decision, decision trees can be your secret weapon for cutting through uncertainty. 

Use free decision tree template

What is a decision tree?

A decision tree is a diagram in the shape of an upside-down tree that shows the different choices and possible outcomes of a decision. It’s essentially a guide for decision-making, with each fork in the road representing a choice you need to make. 

Every decision tree has three main parts: 

  • Nodes: These are points where decisions are made or outcomes are shown. 
  • Branches: These connect nodes and represent the available choices or outcomes. 
  • Leaves: These are the final outcomes at the end of each path. 

This simple structure makes complex decisions easier to understand and communicate. Unlike complicated algorithms or dense spreadsheets, decision trees show your thinking process in a way that almost anyone can follow.

What is a decision tree used for?

Decision trees aren’t just theoretical tools — they have real-world applications across many fields, including: 

  • Classification: Decision trees categorize data into distinct groups. For example, an email service might use a decision tree to sort messages into “spam” or “not spam” based on specific characteristics. 
  • Regression: Decision trees can predict continuous values, like estimating how much a customer might spend based on their browsing history. 
  • Business decision analysis: Companies utilize decision trees to evaluate options when launching new products, entering new markets, or making investment decisions. 

You'll find decision trees being used for customer segmentation (determining which customers are likely to respond to specific offers), medical diagnoses (helping doctors rule out conditions based on symptoms), and loan approvals (deciding whether an applicant is likely to repay their loan).

When cross-functional teams face complex decisions, decision trees provide a common language everyone can understand.

How decision trees work

Decision trees break down complex decisions into a series of simpler choices. The process starts with a single question at the top node, then branches out based on possible answers. 

At each node, the tree asks a question about one specific feature of your data. For example: "Is the customer over 30 years old?" or "Has this person missed a payment in the last year?" The answers determine which branch to follow.

The math behind this process involves concepts like:

  • Splitting: Dividing data into subsets based on feature values
  • Impurity measures: Calculating how mixed the data is at each node 
  • Decision paths: The sequence of decisions that leads to a particular outcome

The goal is to create splits that most effectively separate the data into meaningful groups. Good decision trees make the most informative splits early on, separating the data as cleanly as possible with each decision.

Types of decision trees

Decision trees come in a few different varieties, each suited to specific purposes:

  • Classification trees: These models predict categories or classes, such as whether a transaction is fraudulent or legitimate.
  • Regression trees: These predict continuous numeric values, such as a house price or a patient's blood pressure.

Some decision trees use binary splits (yes/no questions), while others use multiway splits (questions with multiple possible answers). Popular models include CART (Classification and Regression Trees), which uses binary splits to build simple yet powerful trees.

The type of decision tree you choose depends on your specific needs and the kind of data you're working with. Strategic planning often benefits from having multiple tree types to analyze different aspects of a business decision.

Advantages and disadvantages of decision trees

Like any tool, decision trees have their strengths and limitations.

Key advantages include: 

  • Interpretability: Anyone can follow the logic — no degree in statistics is required.
  • Minimal data preparation: They work with both numerical and categorical data without much preprocessing.
  • Versatility: They can handle various types of problems and data.
  • Visual clarity: The tree structure makes complex decisions easier to understand.
  • Handles missing values: Many algorithms can work around incomplete data.
     

Notable limitations are: 

  • Overfitting: Without proper constraints, trees can become too complex and perform poorly on new data.
  • Instability: Small changes in data can sometimes result in completely different trees.
  • Bias toward features with many levels: Trees can give too much importance to variables with numerous categories.
  • Limited precision for certain problems: Some complex relationships can't be captured well by the tree structure.


Understanding these trade-offs helps you decide when to use decision trees and when other methods might be more appropriate. Effective decision-making often involves knowing which tool fits which situation.

How to create a decision tree

Creating an effective decision tree involves five key steps. Whether you're using specialized software or drawing one by hand, following this process will help you build a decision tree that genuinely improves your decision-making.

Define the problem and goal

Identify what decision you need to make. Are you trying to predict customer behavior? Diagnose a problem? Choose between investment options?

Be specific about:

  • What question you're trying to answer
  • What your target variable is (what you're trying to predict or decide)
  • What inputs might influence this outcome


This clarity ensures your decision tree addresses the right problem. During brainstorming sessions, teams often discover they're solving different problems. A decision tree forces everyone to align on exactly what question needs to be answered.

Collect and prepare your data

Good decisions require good data. Gather information that is:

  • Relevant to your decision
  • Complete as possible
  • Accurate and up-to-date
     

Clean your data by handling missing values, correcting errors, and formatting consistently. You may need to encode categorical variables (like colors or customer types) numerically. The quality of your decision tree is directly linked to the quality of your data.

Many teams use knowledge sharing platforms to collect and organize this information, especially when input is needed from multiple departments.

Choose the best feature to split

This is where the science comes in. You need to determine which factor will most effectively separate your data into meaningful groups.

Decision tree algorithms use measures like:

  • Gini impurity: Measures how frequently a randomly chosen element would be incorrectly labeled
  • Information gain: Calculates how much uncertainty is reduced by splitting on a particular feature
  • Chi-square test: Determines if there's a significant relationship between categorical variables

The goal is to find the split that creates the clearest separation between outcomes. Tools like the DACI framework can help teams evaluate which factors should influence key decisions.

Split the dataset

Once you've identified the best feature, divide your data based on the values of that feature. Each split should move you closer to a clear decision.

Let’s take a look at a decision tree example: If you're deciding which projects to prioritize and you've determined that business impact is the most important factor, you might split projects into groups based on impact levels:

  • High impact (strategic goals)
  • Medium impact (operational improvements)
  • Low impact (nice-to-have features)


Each branch should move you closer to a decision about project prioritization. Effective process mapping can help visualize how these splits create different paths toward decisions.

Repeat the process

Continue splitting each branch using the best available features for that subset of data. Keep going until you reach a stopping condition, such as:

  • Maximum tree depth reached
  • All samples in a node belong to the same class
  • Further splitting wouldn't significantly improve results
  • Minimum number of samples per leaf reached

Remember that the deeper your tree, the more complex and potentially overfit it becomes. Balance detail with generalizability for the best results.

Best practices for using decision trees

To get the most from your decision tree maker, follow these proven practices:

  • Pruning for performance: Just as gardeners prune trees for better growth, data scientists "prune" decision trees by removing branches that don't significantly improve predictions. This reduces overfitting and makes the model more reliable on new data.
  • Handle missing values appropriately: Rather than discarding data with missing values, use strategies like surrogate splits (using correlated variables as replacements) or sending missing values down both paths and averaging the results.
  • Balance class distributions: If you're predicting rare events, make sure your tree doesn't predict the most common outcome each time. Techniques like oversampling minority classes or using weighted metrics can help.
  • Validate with new data: Always test your decision tree on data it hasn't seen before to ensure it generalizes well.

These practices help ensure your decision trees provide genuine insight rather than just memorizing your training data. Strong project collaboration tools can help teams implement these practices consistently.

Create effective decision trees with Confluence

Visual tools can significantly enhance how you build and share decision trees. Confluence whiteboards make it easy to create, refine, and collaborate on decision trees.

With online whiteboards in Confluence, you can:

  • Draw decision trees that everyone can access and understand
  • Collaborate in real-time as teams refine decision points
  • Add context and supporting documentation directly alongside your tree
  • Share your decision logic with stakeholders across the organization

This approach ensures your decision trees aren't just theoretical exercises but practical tools that drive better outcomes. The visual nature of decision trees makes them ideal for team collaboration, enabling everyone to understand complex decisions at a glance.

Ready to build better decision trees that lead to smarter choices? Use the free decision tree template

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