Decision Tree Machine Learning: Types & Examples, Use Cases

Have you ever wondered how Netflix knows what series you should binge on or how to keep your email inbox from cluttering up with spam? Machine learning enables all of these. In our daily lives, we interact with various machine learning applications and use them without knowing we are doing so. One of the most powerful tools in the machine learning umbrella is a decision tree. But what is decision tree machine learning? And what is its real-life application? Let’s find out! 

What is Decision Tree Machine Learning?

A decision tree is a flowchart-like structure used in machine learning to make decisions or predictions based on data. It starts with the main node, which is a question or a condition and then branches out to all possible responses or outcomes. Every internal node shows a read, which is based on some features; every branch stands for the result of that decision; and every leaf node forms the final decision or prediction. 

In simple words, imagine you are a hiring manager trying to decide whether to interview a job candidate. Your decision tree machine learning might look like this: 

• The root node asks if the candidate has a relevant degree
• In case they do, the next node asks if they have more than three years of experience
• An affirmative answer then leads on to the decision to interview them
• If they don’t, the decision might be not to interview unless they have exceptional skills or recommendations
• Furthermore, if the candidate does not have a relevant degree, the tree might branch into whether they have significant industry experience or pertinent certifications that could compensate for the lack of a degree

Above all, this structured approach ensures that all potential scenarios are considered systematically.

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The Terminologies of a Decision Tree

To fully grasp decision tree machine learning, it’s important to understand the key terminologies:

1. Root Node

This is the topmost node in a decision tree representing the entire population or sample. It splits into two or more homogeneous sets. For example, in a decision tree for evaluating loan applications, the root node might ask, “Is the applicant’s credit score above 700?”

2. Splitting

This is the process of dividing a node into two or more subnodes. Following the example above, splitting might occur based on whether the applicant’s income is above a certain threshold.

3. Decision Node

When a subnode splits into further subnodes, it is called a decision node. In our loan application example, a decision node could ask, “Does the applicant have a stable job?”

4. Leaf/Terminal Node

Nodes that do not split further are called leaf or terminal nodes, representing the final decision or outcome. For instance, a terminal node could represent the final decision to approve or deny the loan.

5. Branch/Sub-Tree

A subsection of the entire decision tree is called a branch or sub-tree. Each branch in our loan example represents a path from one question to the next.

6. Parent and Child Nodes

The root node of a sub-tree is the parent, and the nodes derived from it are the children. For example, the node that asks about the applicant’s credit score is the parent node, and the subsequent nodes asking about income and job stability are the child nodes.

7. Pruning

This process removes the subnodes of a decision node, often to reduce complexity and avoid overfitting.

Types of Decision Tree

Now, let’s talk about the different types of decision trees. It is a bit like choosing the right tool for the job:

• Classification Trees: Think of these when you need to categorize something. For instance, deciding if an email is spam or not. The tree ends with a category or class label, such as “spam” or “not spam”
• Regression Trees: These come into play when your target is a continuous value, like predicting the price of a house. Instead of a category, you get a specific number, such as a dollar amount
• CART (Classification and Regression Trees): The best of both worlds! CART models can handle both classification and regression tasks, making them incredibly versatile

In addition, each type of decision tree machine learning is tailored for different kinds of prediction tasks, making them a flexible and powerful tool in the machine learning toolbox.

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Real-World Applications of the Decision Tree

You may still be wondering how this abstract principle applies to your daily life. As previously stated, decision tree machine learning can be utilized for classification and regression applications. This makes it extremely adaptable and appropriate for a wide range of challenges. Let’s get into some particular use scenarios:

1. Financial Analysis

Decision trees are invaluable in the financial sector. They can analyze a vast amount of financial data to predict future trends, assess credit risks, and assist in investment decision-making. 

For instance, as we saw in the previous loan-related scenario, a bank can use decision trees to evaluate loan applications by examining factors such as income level, credit score, employment history, and existing debts. The decision tree helps in determining whether an applicant is likely to default on a loan, thereby aiding in making informed lending decisions.

2. Quality Control

In manufacturing, decision trees play a crucial role in maintaining product quality. They can analyze data from various stages of the production process to identify patterns and pinpoint potential defects. 

For example, a car manufacturer might use a decision tree to analyze data on production conditions, material quality, and worker performance. By identifying the conditions under which defects are most likely to occur, the manufacturer can implement corrective measures to improve overall product quality and reduce waste.

3. Healthcare

Decision tree machine learning is extensively used in healthcare for diagnostic and treatment purposes. They help doctors and healthcare professionals make more accurate diagnoses by considering a patient’s symptoms, medical history, and test results. 

For example, a decision tree can be used to diagnose whether a patient has diabetes by analyzing factors such as age, weight, blood pressure, and glucose levels. Additionally, decision trees can assist in recommending personalized treatment plans by considering the patient’s unique medical conditions and history.

4. Decision-Making

Organizations use decision trees to make strategic decisions across various domains. Whether it is deciding on new product launches, optimizing supply chains, or determining marketing strategies, decision trees provide a clear framework for evaluating different options and outcomes. 

For instance, a retail company might use a decision tree to decide on the optimal inventory levels for different products based on factors such as seasonal demand, sales history, and market trends. This helps in reducing stockouts and overstock situations, thereby improving operational efficiency.

5. Fraud Detection

In sectors like banking and e-commerce, detecting and preventing fraud is crucial. Decision trees help in identifying fraudulent activities by analyzing transaction patterns and highlighting anomalies. 

For example, a bank might use a decision tree to monitor credit card transactions for unusual activity. Factors such as transaction amount, location, and frequency are analyzed to determine whether a transaction is likely to be fraudulent. If the decision tree flags a transaction as suspicious, further investigation can be conducted to confirm and prevent potential fraud.

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To sum up, decision trees are a powerful tool that can transform how you approach data and decision-making. Understanding the basics of decision tree machine learning is just the beginning. Whether you’re aiming to optimize business operations, enhance customer experiences, or drive innovation in your field, mastering decision trees is a valuable step. Check out Emeritus’ online artificial intelligence courses and machine learning courses and discover how you can transform your career and business!

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