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Understanding Decision Tree In AI: Types, Examples, and How to Create One

Understanding Decision Tree In AI: Types, Examples, and How to Create One

Updated on Aug 20, 2025 | 15 min read | 22.52K+ views

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What is a Decision Tree in AI?
Types of Decision Trees
Steps to Create a Decision Tree
Examples of Decision Trees in AI
Advantages and Disadvantages of Decision Trees
Best Practices for Using Decision Trees
Conclusion
How Can upGrad Help You Master Decision Trees?

What if there were a system that could help you make accurate predictions and informed decisions every time? A decision tree in AI does exactly that. It uses structured data to guide decisions through a series of logical steps, ensuring precise outcomes at each branch.

In this blog, we’ll break down the concept of a decision tree in AI, explore its types, including classification, regression, multi-value, and categorical/continuous trees, applications, and technologies, and tell you how to create one.

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What is a Decision Tree in AI?

A decision tree in AI is a type of machine learning model that can make predictions based on data. It is represented as a series of decisions and their possible consequences in a tree-like structure. Each decision leads to a further set of decisions, ultimately leading to an outcome.

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Here are the key characteristics of a decision tree in AI.

Hierarchical structure
The tree begins from a single root and branches out into sub-branches and leaves. The tree can be shallow or deep, depending on the complexity of the decisions. For example, for a simple Tic-Tac-Toe game, the decision tree is shallow, but for a Chess game, it is deeper.
Nodes
Nodes represent decision points where a question or test is asked about the data. For example, "Is age > 30?" or "Is income greater than INR 50,000?" represent nodes.
Branches
Branches are the paths that connect nodes. They represent the answers to the questions asked at each node. For example, “Is age > 30?” If Yes, go to “Branch1”, else go to “Branch2”.
Leaves
Leaves represent the terminal points of the tree that give the final decision or prediction. For example, if a question is “should I buy a Samsung phone based on the current prices?”, "Buy" or "Don't Buy" are the end decisions.

Here are the applications of the decision tree in AI.

Classification
Decision trees can be used in classification tasks, where the end objective is to predict a category or class. For example, whether the email is spam or not spam, based on evidence.
Regression
Decision trees predict continuous values in regression tasks. For example, decision trees can predict house prices based on features like size, location, etc.
Decision-making
Decision trees are used to model decisions in various applications, helping to choose the best course of action by evaluating different criteria and their outcomes.

Also Read: Regression Vs Classification in Machine Learning: Difference Between Regression and Classification

Here are some of the key technologies used in the decision tree in AI.

1. Splitting Criteria: The following technologies are used for splitting criteria.

Gini Impurity: It measures the purity of a split. A lower Gini impurity means the split is better because it divides the data into more homogeneous groups.
Entropy: It is used to determine the "information gain" after a split. It measures the uncertainty in the data before and after the split.
Mean Squared Error (MSE): It measures how well the splits reduce the variance within the subsets.

2. Tree Construction: Here are the technologies used for tree construction.

Recursive Binary Splitting: The algorithm splits the data into two groups at each node, choosing the best feature and split that minimizes the chosen criterion.
CART: CART builds the tree by recursively splitting the dataset into subsets based on feature values. At each step, the algorithm looks for the feature and threshold that best separates the data.

3. Pruning: Here are the two methods of pruning.

Pre-pruning: Limits the tree’s growth by setting constraints such as maximum depth or minimum samples per node to prevent overfitting.
Post-pruning: Prunes the tree after it’s fully grown. It removes branches that provide little predictive value to improve generalization.

4. Handling missing data

Decision trees use strategies like surrogate splits to handle missing data, where alternative splits are considered when the main feature is missing.

5. Random Forests

In this technology, multiple trees are trained on different subsets of data and combined to make a more robust prediction.

6. Overfitting Control: Here are the technologies used to control overfitting.

Max Depth: Limits the tree’s depth to prevent it from growing too large and overfitting the data.
Minimum Samples per Split: It reduces the minimum number of samples required to split a node.
Minimum Samples per Leaf: Ensures that a node has enough data points before it's considered as a terminal leaf.

After this brief overview, let’s check out the different types of the decision tree in AI.

Types of Decision Trees

Decision trees come in different types, each suited to perform specific decision tasks. Here is the classification of a decision tree in AI.

1. Classification Trees

Classification trees can predict categorical outcomes. They split data at each node based on a feature that best separates the classes. The goal is to assign each data point to a specific class.

Applications:

It can classify incoming emails as spam or not spam.
Categorizing customers into groups for targeted marketing based on purchasing behavior.

2. Regression Trees

Regression trees can predict continuous numerical outcomes. They predict numerical values by dividing data into subsets based on features, minimizing the data variation within each subset.

Applications:

Estimating the house price based on features like location, age, and square footage.
Predicting future sales based on historical data, marketing conditions, and seasonality.

3. Multi-Value Decision Trees

Multi-value decision trees can handle multiple possible outcomes at each decision node. They can deal with scenarios where the decision can lead to more than two possible outcomes or classes.

Applications:

E-commerce platforms use multi-value trees to recommend products by considering factors like preferences and browsing history.
Financial institutions use these trees to classify loan applicants into multiple risk levels based on criteria like credit score and income.

4. Categorical and Continuous Trees

These trees are designed to handle a mixture of data types in a single model, making them suitable for more complex datasets.

Applications:

In healthcare, these trees use categorical data like "gender" or "smoking status" and numerical data like "age" or "cholesterol level" to predict health risks.
Used in financial systems, where categorical data like "transaction location" and continuous data like "transaction amount" are used to detect fraud.

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Now that you’ve understood the types of decision tree in AI, let’s explore the steps to create a decision tree.

Steps to Create a Decision Tree

Decision trees are created by following a structured and systematic process that involves making decisions based on specific criteria to achieve a desired outcome.

Here are the steps to create the decision tree in AI.

1. Define the Problem

The first step is to understand the problem you're solving and decide whether it's a classification or regression task. It will help you in selecting appropriate techniques and evaluation metrics.

If the problem involves assigning labels to data points (ex, classifying emails as "spam" or "not spam"), you're dealing with classification. If you're predicting a continuous value (ex, house prices or stock prices), then regression is the goal.

2. Prepare the Dataset

The performance of your decision tree depends upon the quality of the data. Here are the steps involved in preparing the dataset.

Data cleaning: Remove or handle missing values, outliers, and erroneous data points. If some records have missing values, decide whether to fill them or remove those records altogether.
Feature selection: Not all features in the dataset may be useful for making predictions. Select the most suitable features that can help the model in making predictions.
Data transformation: If the features are on different scales (ex, if salary is in thousands while age is a single digit), you may need to standardize or normalize the data.
Split Data into training and testing sets: After preparing the dataset, divide it into two subsets. Around 70-80% of the data is used to train the model. The remaining 20-30% is kept aside as unseen data for testing purposes.

Also Read: 11 Essential Data Transformation in Data Mining Techniques

3. Select Splitting Criteria

Make the tree understand how to split the data into smaller, more homogenous subsets at each node. Use the following criteria to help the tree split.

Gini Index: Measures the "impurity" of a node. A value of 0 means perfect purity (all data points belong to a single class), while a value of 1 indicates maximum impurity (data points are evenly split across all classes).
Entropy (Information gain): Entropy measures the disorder or impurity in the data. Information gain is used to determine which feature will most effectively split the data into groups that are as pure as possible.
Mean Squared Error (MSE): MSE calculates the variance within the nodes. The tree minimizes this error by selecting the splits that best reduce the variance in the target variable.

4. Build the Tree:

The decision tree is built by recursively splitting the data at each node. The algorithm evaluates all possible splits for each feature at every level and selects the one that best separates the data. Here are the two methods used to build a decision tree.

Recursive splitting: In this step, the tree asks another question (based on another feature) to further divide the data. This step is repeated until certain stopping criteria, such as maximum depth or minimum number of samples, are met.
CART (Classification and Regression Trees): CART uses binary splits to build the tree by choosing the best split that minimizes the Gini Index (for classification) or Mean Squared Error (for regression).

5. Prune the Tree

After the tree is built, it may become too complex and start fitting the noise in the training data, leading to overfitting. Pruning removes unnecessary branches to prevent overfitting. Here are the different pruning processes.

Pre-Pruning: This method sets constraints (ex, maximum tree depth) during tree construction to prevent overfitting.
Post-Pruning: It evaluates the tree and cuts branches after the initial construction. For example, a branch that improves the accuracy by a small margin might be pruned away.

6. Validate the Model

After building and pruning the decision tree, you must test its performance on the testing set. This ensures that the tree has learned to generalize, not just memorize the training data. Here’s how you can validate the model.

Accuracy: For classification tasks, accuracy (the percentage of correct predictions) is used to evaluate model performance. For regression tasks, use metrics like Root Mean Squared Error (RMSE) or Mean Absolute Error (MAE).
Cross-Validation: It is a more robust way of validation. Here, the dataset is divided into multiple folds, and the tree is trained and tested on different subsets of the data. This helps in assessing the model’s stability and preventing overfitting.

Also Read: What is Overfitting & Underfitting In Machine Learning?

Now that you’ve learned how to build a decision tree in AI, let’s take a look at some real-world examples of decision trees in action.

Examples of Decision Trees in AI

You can use decision trees across different domains, ranging from education and healthcare to finance and customer service. The following decision tree in artificial intelligence examples show how decision trees can classify data or predict outcomes based on various input features.

1. Loan Approval Prediction

Banks or lending institutions use decision tree to predict the approval of a loan application. The model analyzes features such as the applicant's employment history, income, credit score, and loan amount.

Based on these factors, the tree can give an outcome of either approval or rejection. The tree automates the decision-making process, making it faster and more consistent.

2. Diagnosing Medical Conditions

In healthcare, decision trees can help doctors diagnose medical conditions based on patient symptoms. For example, if a patient has symptoms like fever, cough, and shortness of breath, a decision tree predicts whether the patient has a condition like the flu or COVID-19.

The tree splits the symptoms at each node, allowing for a quick diagnosis based on a series of "yes" or "no" questions, which is beneficial in time-sensitive situations.

3. Customer Churn Analysis

Telecommunication companies and subscription-based businesses use decision trees to predict customer the likelihood that a customer will stop using the service.

The decision tree considers factors like usage patterns and customer satisfaction to identify customers at risk of leaving. By identifying patterns in the data, companies can take proactive steps to retain customers.

4. Predicting Exam Results

Educational institutions can predict whether students will pass or fail exams using a decision tree. Factors like their study habits, attendance, participation in class, and previous academic performance are used as criteria.

For instance, the tree may suggest that students who study for more than 10 hours a week and have attendance above 80% are more likely to pass. Teachers can identify students who may require extra support or intervention.

5. Predicting Employee Performance

HR departments can use decision trees to predict employee performance based on factors such as job experience, skills, and attendance. The tree helps managers identify employees who might need additional training or support. This can help improve team productivity and retention.

After exploring real-world applications of decision trees in AI, let’s take a look at their advantages and limitations.

Advantages and Disadvantages of Decision Trees

Decision trees are powerful tools in machine learning, but like any model, they come with both strengths and weaknesses. Understanding these advantages and disadvantages helps you choose when and how to use decision trees effectively.

The decision tree in AI has the following advantages.

Factor	Description
Simple to understand and interpret	Decision trees are easy to visualize and interpret, making them accessible even for those without a strong background in machine learning. The flowchart-like structure is useful for explaining results to non-technical stakeholders.
Handles categorical and numerical data	Decision trees can handle both categorical and continuous data types without requiring data transformation. Whether you're working with customer demographics (categorical) or sales data (numerical), a decision tree in AI can handle both.
Does not need feature scaling or normalization	Unlike some other machine learning algorithms (such as k-nearest neighbors), decision trees do not require feature scaling input data. This saves preprocessing time.
Handle non-linear relationships	Decision trees can capture non-linear relationships between features. The non-linear relationships are captured by creating a series of splits based on features.
Handles missing values	Decision tree algorithms can manage missing data by finding the best split for records with missing values or by using surrogate splits to approximate missing data points.

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Here are the disadvantages of using a decision tree in AI.

Factor	Description
Prone to overfitting	The decision tree is prone to overfitting when the tree is deep and complex. This means that the tree may perform excellently on the training data, but it might not generalize well to new, unseen data.
Imbalanced dataset can make it biased	In a classification task where 90% of the data belongs to one class and 10% to another, the tree may predict the majority class, ignoring the minority class. This can lead to poor performance.
Less effective with complex datasets	Decision trees struggle with highly complex datasets that involve many variables or intricate relationships between features. The tree may create a complex structure, leading to poor generalization.
Instability with small data changes	A minor variation in the training dataset can lead to a completely different tree structure. Random Forests are often preferred due to the lack of stability of decision trees.
Computationally expensive	Building a tree requires recursively splitting the data at each node, which can become time-consuming as the dataset grows. This can also make computation expensive.

Also Read: How to Create Perfect Decision Tree?

After reviewing the benefits and limitations of decision trees in AI, let’s now explore the best practices for using them effectively.

Best Practices for Using Decision Trees

The performance of the decision tree in AI depends on how they are prepared and applied. Best practices will ensure that your decision tree model is both accurate and efficient.

Here are the best practices for the decision tree in AI.

Preprocess data to ensure quality and balance
A balanced dataset prevents the tree from becoming biased towards the majority class. If the dataset is imbalanced, use techniques like oversampling, undersampling, or weighted classes to make the decision tree learn effectively from all classes. Also, check for any missing values, outliers, or irrelevant features before processing.
Use feature selection to improve efficiency
Feature selection improves the efficiency of the model by identifying and using only the most important variables. This speeds up training time and also reduces the risk of overfitting. Choose the best features using methods like information gain, Gini index, or mutual information.
Prune the tree to enhance generalization
Pruning reduces the size of the decision tree by removing branches that add little predictive power. By simplifying the tree, you improve its ability to generalize to unseen data, making it more robust and effective.
Combine decision trees with ensemble methods
Combining multiple trees using ensemble methods like Random Forest or Gradient Boosting often leads to better performance. The ensemble methods utilize the strengths of decision trees while mitigating their individual weaknesses.

After exploring the concept of the decision tree in AI, let’s look at how you can build a career in this field.

Conclusion

Decision trees in AI stand out as one of the most versatile and intuitive machine learning models, offering clarity, accuracy, and scalability across industries. From predicting loan approvals to diagnosing medical conditions, they simplify complex problems into structured, logical steps.

While they come with challenges like overfitting and instability, best practices such as pruning, feature selection, and ensemble methods ensure their effectiveness. As organizations increasingly adopt AI-driven solutions, mastering decision trees is essential for anyone aspiring to build a strong career in data science.

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How Can upGrad Help You Master Decision Trees?

In the broader context of machine learning, decision trees form the foundation for more complex models like Gradient Boosting. Additionally, decision trees are integral to many AI and data science applications, helping companies automate decision-making and predict outcomes.

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Frequently Asked Questions (FAQs)

1. What causes underfitting?

Underfitting occurs when the model is too simple to capture the underlying patterns in the data, often due to insufficient features or complexity.

2. Which is better: Gini or Entropy?

Both Gini and Entropy are used for splitting in decision trees, but Gini is faster to compute, while entropy is more informative.

3. What is impurity in a decision tree?

Impurity in a decision tree refers to how mixed the classes are in a node. A node with pure classes has low impurity, while a node with a mix of classes has high impurity.

4. What is the output of a decision tree in AI?

The output of a decision tree in AI can be a predicted class label for classification tasks or a continuous numerical value for regression tasks. Based on input features, the model navigates through decision nodes until it reaches a leaf node that provides the final prediction.

5. Why use a decision tree in AI?

A decision tree in AI is widely used because it is easy to understand, interpret, and visualize. It can handle both numerical and categorical data with minimal preprocessing. Its simple yet powerful structure makes it effective for decision-making, classification, and regression tasks across industries.

6. What are some decision tree in artificial intelligence examples?

Common decision tree in artificial intelligence examples include email spam filtering, loan approval systems, disease diagnosis, fraud detection, and customer churn prediction. These applications showcase how decision trees simplify complex decision-making by breaking problems into smaller, interpretable steps for accurate and efficient predictions.

7. How do you draw a decision tree in AI?

To draw a decision tree in AI, begin with a root node representing the initial decision point. Split the dataset recursively using the best feature until reaching leaf nodes. Each branch represents a condition, while leaves represent the final outcome, prediction, or classification.

8. What is entropy in a decision tree?

Entropy in a decision tree measures data impurity or uncertainty. It helps determine the best feature for splitting data at each node. Lower entropy means purer subsets, making it a crucial factor in deciding splits for classification tasks in AI.

9. Which is better: Gini or Entropy for decision trees?

Both Gini Index and Entropy are used to measure impurity in decision trees. Gini is computationally faster, making it efficient, while Entropy provides more detailed information. The choice often depends on the dataset and the performance goals of the AI model.

10. What is impurity in a decision tree?

Impurity in a decision tree refers to how mixed the data classes are in a node. Pure nodes contain samples from a single class, while high-impurity nodes have mixed classes. Reducing impurity helps improve model accuracy by creating better splits.

11. What are the problems with decision tree learning?

Problems with decision tree learning include overfitting, sensitivity to noisy or imbalanced data, and instability—where small changes in data can lead to a different tree. Additionally, decision trees may perform poorly when handling complex patterns compared to ensemble methods like Random Forest.

12. Why do decision trees overfit in AI?

Decision trees overfit when they grow too deep and capture noise along with meaningful patterns. Overfitting reduces generalization and accuracy on unseen data. Techniques like pruning, setting maximum depth, or using ensemble approaches can minimize this issue and improve performance.

13. What causes underfitting in decision trees?

Underfitting in decision trees occurs when the model is too simple to capture relationships within data. Shallow trees, limited features, or restrictive splitting criteria can lead to poor accuracy. Increasing depth or adding relevant features helps address underfitting issues effectively.

14. What is the difference between bagging and boosting in AI?

Bagging builds multiple decision trees independently on random data subsets and averages results to reduce variance. Boosting builds trees sequentially, focusing on errors made by prior models. Both improve decision tree performance, but boosting generally yields higher accuracy with more computational cost.

15. What is pruning in a decision tree in AI?

Pruning is the process of trimming unnecessary branches from a decision tree in AI. It prevents overfitting by removing parts of the tree that do not contribute significantly to prediction accuracy. This results in simpler, more generalizable, and efficient models.

16. What is a decision node in a decision tree?

A decision node in a decision tree represents a feature-based split in the dataset. Each branch from the node leads to further divisions or outcomes. Decision nodes guide the tree structure, helping classify or predict results based on feature conditions.

17. What is CART in decision tree learning?

CART (Classification and Regression Trees) is a popular decision tree algorithm. It can handle both classification and regression tasks by splitting nodes based on measures like Gini Index. CART is widely used in AI for building predictive models that are interpretable and efficient.

18. What types of decision trees are used in AI?

Types of decision trees in AI include Classification Trees for categorical outputs, Regression Trees for continuous outputs, and hybrid models. These trees are applied across domains like healthcare, finance, and marketing to solve diverse predictive modeling challenges.

19. How do decision trees compare with Random Forests?

A single decision tree is interpretable but prone to overfitting, while Random Forest combines multiple trees to improve accuracy and reduce variance. Although Random Forests are less interpretable, they often outperform standalone trees in real-world AI applications.

20. How are decision trees applied in machine learning pipelines?

In machine learning pipelines, decision trees act as base models for classification or regression. They are often combined with preprocessing steps, feature engineering, and ensemble methods to enhance performance. Their flexibility makes them integral to AI workflows.

21. What is the role of leaf nodes in a decision tree?

Leaf nodes represent the final outcomes or predictions in a decision tree. Each path from the root through decision nodes ends at a leaf, which outputs either a class label (for classification) or a value (for regression).

22. How do decision trees handle categorical vs numerical data?

Decision trees in AI handle categorical data by splitting based on categories, while numerical data is divided using threshold values. Their ability to process both types makes them versatile and applicable across diverse datasets and real-world AI use cases.

23. What are the advantages of using a decision tree in AI?

The main advantages of a decision tree in AI are interpretability, flexibility, and minimal preprocessing. They can handle both linear and non-linear relationships, making them practical for various domains. Their visual structure makes them easy to explain to stakeholders.

Pavan Vadapalli

900 articles published

Pavan Vadapalli is the Director of Engineering , bringing over 18 years of experience in software engineering, technology leadership, and startup innovation. Holding a B.Tech and an MBA from the India...

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