What is a decision tree in machine learning?

In day-to-day scenarios, the process of breaking down and examining your decisions well is quite challenging. However, if done well, it can help you effectively determine potential outcomes, assess the associated risks and also predict your chances for success.

This is where a decision tree in machine learning comes in.

Decision trees are a powerful tool in the world of machine learning and data analysis. They help make decisions by breaking down complex problems into simpler, more actionable steps.

In this detailed guide, we will explain what a decision tree is, how it works, the types of decision trees, the advantages and disadvantages of a decision tree, and how IEEE BLP courses can help you learn more about decision trees in machine learning.

What Is a Decision Tree?

With the machine learning market projected to reach US$79.29 billion by 2024, its applications span various industries.

A decision tree represents one such application, which serves as a graphical tool to predict decision-making processes by mapping multiple courses of action and their potential outcomes.

Similar to flowcharts, decision trees begin at the root node with a specific question or piece of data, leading to branches with potential outcomes.

They then lead to decision nodes, which come up with more potential questions and the process goes on till the data reaches a terminal or leaf node, where it ends.

How Does the Decision Tree Work?
To be able to predict the outcome of the given dataset, the decision tree algorithm begins from the root node. The algorithm does a comparison of the values of the root attribute with the record attribute and, accordingly, moves to the next node.

The workings of a decision tree can be explained with the steps mentioned below

Repeat the Process: The tree then repeats the process of following branches till you reach a leaf node, which highlights your outcome or decision.

Begin at the Root Node: Start at the top of the decision tree, which is the root node, and answer the question or consider the possible condition presented there.

Follow the Branches: Based on your answer, make sure to follow the corresponding branch to the next node or the internal node.

Different Types of a Decision Tree

Here are the top two types of decision trees that are commonly used in machine learning:

1. Classification Trees

Classification trees, also known as categorical variable decision trees, use an algorithm that categorizes data based on input features. Each data point passes through nodes, where it is classified into different categories at the leaf nodes.

These trees are adept at handling yes-or-no questions, making them valuable for practical applications such as:

• Determining if a shipment was complete.
• Assessing the success of training sessions.
• Evaluating customer service experiences.

2. Regression Trees

Regression trees, also known as continuous variable decision trees, predict outcomes based on input features, aiming to predict a continuous value. These decision trees are composed of branches, nodes, and leaves. Every node here represents a feature, whereas every leaf highlights a potential outcome.

They are typically constructed using historical data and are used for tasks such as:

• Predicting foot traffic at stores during the holidays.
• Estimating the number of employee promotions in a quarter.
• Forecasting monthly product sales.

These decision tree types serve distinct purposes in machine learning, catering to both classification and regression tasks based on the nature of the output they aim to predict.