What is Decision Trees in AI: Know it Approaches and Applications

Table of Contents

Do you know what is a Decision Tree?

Decision trees are one of the most essential and powerful tools. It is very useful for handling classification and regression tasks in artificial intelligence (AI). Decision trees work by breaking down difficult problems into simple and more manageable tasks. This article explains what is decision trees. Additionally, it also explores their approaches, applications, and significance in the field of AI.

What are Decision Trees?

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A decision tree is a machine-learning algorithm. It can be used for both classification and regression problems. But usually, it’s preferred for classification problems. The decision trees start from a root node and branch out into several decision nodes. Hence, it’s called a decision tree. It branches out based on whether the answer to a question is yes or no. The branches eventually lead to leaf nodes, which represent the outcomes or classifications. A leaf node has no further branches. Clearly, we can say that a decision tree is a graphical representation for visualizing all the possible solutions to a problem subject to the given conditions.

Approach to Decision Trees

A graphical representation of a problem helps to understand the problem visually and gives better solutions. When we use a decision algorithm tree to solve a problem, we visualize it as an inverted tree. In a decision tree, the root is at the top. We analyze the problem based on the given conditions and spread the branches downwards.

The initial position in the problem is called the root node. We analyze the data set and the options available for us at the root node and split the tree into further branches depending on the options. The outcome of the final branch is called the leaf node. Additionally, the number of branching levels will depend upon the complexity of the data set.

This approach is similar to the If–Else statements in any programming language.

Decision Tree Algorithms

1. Iterative Dichotomiser 3

In the year 1986, Ross Quinlan developed Iterative Dichotomiser 3(ID3). It is one of the earliest algorithms for constructing decision trees. ID3 works by choosing the best attribute at each node based on maximum information gain. The maximum information gain is attained by the reduction in entropy or uncertainty. While ID3 is efficient, it favors attributes with a large number of values and can create biased trees.

2. C4.5

C4.5 is an improvement over ID3 by Quinlan in 1993. It addresses some of the limitations of ID3. It utilizes the information gain ratio as the splitting criteria instead of pure information gain. Additionally, C4.5 is capable of handling missing values and also it can handle both discrete and continuous data.

3. Classification and Regression Trees

In 1984, Leo Breiman developed Classification and regression trees (CART). It constructs binary trees by recursively splitting the data into two subsets based on the best split at each node. It uses the Gini impurity or the mean squared error as the splitting criteria. CART works for both classification and regression tasks.

4. Random Forests

Random forest is a supervised learning algorithm. It uses multiple decision trees to improve performance and robustness. Data scientists prefer it as it is very accurate. However, the drawback of random forests is that the computation time might be longer for extensive datasets. Random forests work for both classification and regression tasks.

Applications of Decision Trees

1. Medical Diagnosis

Decision trees help healthcare professionals in diagnosing diseases. The results are based on medical data such as symptoms and risk factors. It analyses data sets of patients and narrows down the possibilities of diseases. Along with diagnosis, it also recommends the appropriate treatment options available for the patient.

2. Detection of Fraudulence

Decision trees are useful in the banking and finance industry. It helps to detect fraud and safeguard assets. In particular, they help analyze transactional data and customer behavior patterns and also monitor suspicious activities.

3. Customer Management

Decision trees are used by companies to attract customers and retain them. They contribute to the organization by helping to understand the customer’s interests and requirements by analyzing their previous orders and search history. As a result, it suggests products tailored to customers’ interests and needs. It also offers personalized interactions and discounts to gain customer satisfaction and retain them long-term.

4. Predictive Maintenance

In industries and manufacturing companies, decision trees can be used for predictive maintenance. The system’s failure can be estimated beforehand by identifying patterns in previous operational data. This helps in reducing operational costs, the overall performance of the equipment, and the smooth flow of production.

5. Optimization of Marketing Campaign

AI in marketing campaigns is a well-known example as we have all experienced it. When we search for a product on Google or an online site, our gadget is filled with various options of the product from different online sites. A decision tree is behind all such marketing campaigns. It keeps track of all our searches and responses to a link and improves the performance of a marketer.

Advantages

Interpretability: Decision trees allow users to understand the reasoning behind each decision and gain knowledge about the underlying data patterns. Thus, it offers transparency and interpretability.
Versatility: It is suitable for a wide range of applications across various domains as it can handle both categorical and numerical data.
Scalability: Decision trees can efficiently handle huge datasets. It can take in increasing volumes of data without any significant computational overhead.
Robustness: Compared to other machine learning algorithms, decision trees are robust to outliers and missing values. Thus, it requires minimal data preprocessing.
Ensemble Learning: Through techniques like random forests, decision trees can be combined to form more powerful ensemble models. Hence, it enhances predictive accuracy and generalization.

Disadvantages

Overfitting: Decision trees are prone to overfitting, especially with complex datasets or when the tree depth is not appropriately controlled. This leads to poor generalization of unseen data.
Bias towards Features: Decision trees favor features with more levels or categories. This may potentially lead to biased splits and suboptimal trees.
Instability: Even tiny variations in the training data end up significantly altering decision trees, making the model less stable and robust.
Inability to Capture Non-linear Relationships: Decision trees are usually linear models. Hence, without additional transformations or feature engineering, it may struggle to capture complex non-linear relationships in the data.

Conclusion

This article explains what is a decision tree and its approaches and applications in detail. Decision trees stand as a vital element in the domain of AI. It offers a powerful framework for decision-making, classification, and regression tasks.

Learn More

To learn more about decision trees systematically, please check out the Machine Learning course by Henry Harvin Education. Henry Harvin is a leading Edtech company present globally. They have a range of structured courses in AI and machine learning to choose from. These courses will give in-depth knowledge of the latest AI technology. With interactive online sessions, assignments, and internships, they are designed to empower people with the necessary skills to master the most demanding technology.

FAQ’s

Q.1 What is a decision tree?

Ans. A decision tree is a machine-learning algorithm. It can be used for both classification and regression problems. But usually, it’s preferred for classification problems.

Q.2 Where are decision trees mainly used?

Ans. Decision trees are widely used in operations research and operations management.

Q.3 Name a few decision tree algorithms.

Ans. ID3, CART, Chi-Square, and Reduction in Variance are some common decision tree algorithms.

Q.4 What are the advantages of a decision tree?

Ans. It helps to visualize and interpret the data.

It can handle both numerical and categorical data.
It can interpret huge datasets.
Through random forest techniques, they form powerful ensemble models.

Q.5 What are decision trees best used for?

Ans. Decision trees are one of the most essential and powerful tools for handling classification and regression tasks in artificial intelligence (AI). They work on breaking down difficult problems into simple and more manageable tasks.

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