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

1. 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.
2. Follow the Branches: Based on your answer, make sure to follow the corresponding branch to the next node or the internal node.
3. Repeat the Process: The tree then repeats the process of following branches till you reach a leaf node, which highlights your outcome or decision.

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.

Different Nodes of the Decision Tree

Decision trees typically consist of three different elements:

1.  Root Node

Root node is always the first node in the path of a decision tree. As the top-level node, it represents the main objective or big decision you will possibly make. So, it is the node from which all other decision and end nodes branch further.

2.  Decision Node (Internal Node)

A number of decision nodes emerge from the root node, representing the outcomes or decisions to be made. Each decision node here symbolizes a question or split point and is represented using square nodes.

3.  Splitting

Splitting or branching is when one particular node divides sub-nodes, which can either be an internal node or lead to a leaf or end node.

4.  Leaf Node

The end nodes in a decision tree are generally known as leaf nodes. These show the end of an outcome or a decision path. A leaf node can be easily identified as it doesn’t split or branch any further, similar to a real leaf.

5.  Pruning

Pruning refers to the process of removing unwanted branches from the decision tree. Put simply, it is a procedure that removes the specific parts of the decision tree that hinder it from growing fully.

6.  Branch

Each branch in a decision tree represents a possible answer to the question at the current node. This means that the branches coming out of a decision node are decision branches, where each of them is the representation of possible outcomes available at a given point. 

Metrics for Splitting

There are some key splitting metrics for splitting in decision trees. Some of these are discussed below:

1.  Entropy

Entropy refers to the degree of disorder, uncertainty, or impurity of a random variable. It can be considered an assessment of the unpredictability of data points. A high order of disorder means a low level of impurity.

2.  Information Gain

Information gain is another splitting metric that refers to the process of identifying the most important features/attributes that highlight the key information about a class.

3.  Gini Impurity

The Gini impurity splitting metrics in machine learning help you predict the chances of a randomly picked example being wrongly classified by a specific node.

Decision Tree Algorithms

Decision trees represent a versatile and easily interpretable machine learning technique applicable to both regression and classification tasks. In essence, a decision tree is a supervised learning algorithm used to classify data and predict outcomes in regression modeling.

By learning basic decision rules from previous training data, a decision tree algorithm can be used to develop a training model that can be used to predict the class or value of the target variable.

Here’s a brief overview of different decision tree algorithms:

1. ID3 (Iterative Dichotomiser 3): ID3 iteratively divides features into groups to construct a decision tree. Starting from the top, it selects the best feature at each step to create nodes.
2. C4.5 CART (Classification and Regression Trees): The requirement that features be categorical is removed by the C4.5 decision tree algorithm. It accomplishes this by dividing the continuous attribute value into a discrete set of intervals through the definition of a distinct attribute.
3. CHAID (Chi-Square Automatic Interaction Detection): CHAID is used for data sets with a nominal target variable. It employs multiway splits on both ordinal and nominal data, merging categories and selecting split variables based on significance.
4. MARS (Multivariate Adaptive Regression Splines): MARS is an extension of linear models, ideal for capturing complex relationships between predictors and target variables without assuming linear relationships.

These algorithms provide various approaches to constructing decision trees tailored to different types of data and modeling needs.

Advantages of Decision Trees

Among the key advantages of decision trees are:

1. Simplicity: Decision trees are quite simple to interpret and understand as they follow human logic and reasoning.
2. Equipped to handle data types: Decision trees are equipped to deal with high-dimensional data as well as complex relationships among features.
3. Minimal data preparation: Another benefit of decision trees is that they can manage various forms of data with minimal preparation and without impacting the performance of the algorithm.
4. Robustness to non-linear parameters: Decision trees are non-parametric in nature, which means they work without making any sort of assumptions about the structure or the distribution of the data.

Disadvantages of Decision Trees

Among the main disadvantages of Decision trees are:

1. High variance: Decision trees can be unstable, which means even small changes or variances in the data can cause big structural changes and predictions of the tree.
2. Bias: Another disadvantage of decision trees is that they can be biased, which means they can favor a specific set of features or classes over others based on splitting criteria.
3. Overfitting: Decision trees can be prone to overfitting, which means they can capture too much variance or noise in the data and perform poorly if the data is new or unseen.

Applications of Decision Trees

Here are some of the common examples and applications of decision trees in machine learning

• Healthcare: Decision trees are used in the healthcare industry to predict various diseases, evaluate various treatment options, and improve overall patient care.
• Robotics: Decision trees are used in robotics for better navigation and control.
• Education: In education, decision trees can be used for educational data mining, classification of data, and prediction of learner/student performance.
• Manufacturing: Decision trees can be used in manufacturing for predictive modeling and forecasting.

Learning Machine Learning through IEEE BLP courses

Decision tree algorithms are among the most useful machine learning algorithms researchers and data scientists need to know about.

Whether you are a student, researcher, or academician, it is crucial to learn decision tree algorithms in machine learning and leverage them for modeling purposes to navigate various data science challenges. 

Data also suggests that as many as 82% of organizations today need machine learning skills, compared to only 12% of enterprises that believe the supply of ML skills is at an adequate level.

To navigate these challenges effectively and to learn more about decision trees in machine learning, enroll in IEEE BLP decision trees in Machine Learning courses and get quick & easy access to the best-curated resources and content. 

FAQs

1.  What is decision tree learning in machine learning?

A decision tree in machine learning is a supervised learning algorithm. It has a tree structure, which consists of a root node, branches, leaves, and internal nodes. The decisions here are performed on the basis of the features of the given dataset.

2.  What is meant by a decision tree?

A decision tree diagram is a kind of flowchart to simplify the decision-making process by breaking down the different paths of outcomes available. It can be considered a graphical representation for getting all the possible solutions to a problem or decision based on given conditions.

3.  What are the types of decision trees?

A categorical-variable decision tree and a continuous-variable decision tree are two different types of decision trees. While there is a categorical variable algorithm in the former, the input features are leveraged to predict a continuous output later.

4.  What is the term for decision trees in machine learning?

A decision tree is a tree-like structure that serves as a decision support tool and displays decisions and their potential outcomes and consequences. From there, the branches of the decision tree can easily be evaluated and compared to be able to select the best possible outcome.