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Decision Trees A Beginner's Guide

 In today's data-driven era, tools to support complex decision-making are constantly emerging.


Among these, “Decision Trees” are a popular method due to their ease of understanding and intuitive visualization.


Here, we will explain the basic concepts of decision trees, specific scenarios where they are used, and the benefits of learning them.


1. What are Decision Trees?


Decision Trees are a type of model used for data classification and prediction. They use a tree structure to represent the decision-making process.


Decision Trees consist of nodes (decision nodes) and edges (branches). Each node involves a conditional judgment based on a certain feature, and the branches diverge based on that result. Ultimately, the classification result or predicted value is indicated at the terminal parts called leaf nodes.


The learning algorithm for decision trees aims to accurately capture the characteristics of the data by determining each branching point to minimise information impurity. This allows even complex data to be broken down into a form that is intuitively understandable.


This mechanism visually clarifies the decision-making process, making the results easy to understand even without specialist knowledge.


2. Where are they Used?


Decision Trees demonstrate their usefulness in a variety of fields. The following are just a few examples.


- Business and Marketing


They are widely used in developing marketing strategies and risk management, such as analysing customer purchasing behaviour, segmenting customers, and credit scoring. The ability to intuitively classify data speeds up and transparentises the decision-making process.


- Healthcare


Decision Trees are being implemented in healthcare settings as tools to support diagnosis based on patient symptoms and test results. They play a role in indicating important patterns obtained from the data for risk assessment and determining treatment plans.


- Financial Industry


Decision Trees are used to understand data trends and predict future risks as an aid for loan repayment risk and investment decisions. Financial data contains multi-layered and multi-dimensional information, and the visual analysis of decision trees significantly contributes to decision-making.


- Education and Research


By using decision trees, complex phenomena can be reduced to simple rules, allowing students and researchers to intuitively understand the theory and practice of data analysis. Implementation in educational settings is particularly useful for beginners in data science.


3. What are the Benefits of Learning Decision Trees?


Learning decision trees has value in many respects.


- Intuitive Understanding and Visualisation


Decision Trees visually represent the results, allowing you to understand the internal structure of complex models at a glance. The flow of data is shown in a tree structure, making it easy to follow how each judgment affects the final result.


- Practical Decision-Making Support


Real-world decision-making in business, healthcare, and finance involves multiple factors. Decision Trees organise these complex factors and clarify which variables have the most influence, greatly contributing to the development of effective measures and strategies.


- Foundations for Introductory Data Science


Decision Trees are also an excellent gateway to more complex ensemble learning (such as Random Forests and Boosting). Understanding the basic concepts allows you to smoothly progress into a wider range of data analysis fields, such as data processing and model evaluation.


- Versatility and Wide Range of Applications


Decision Trees are applicable to diverse datasets and problem settings, making them a valued practical skill in a wide range of industries. The ability to directly link analytical results to real-world problem-solving is also a major advantage.


In Summary


Decision Trees, with their simple and intuitive structure and wide range of applications, play a powerful role as a modern data analysis technique. By learning decision trees, which have achieved results in various fields such as business, healthcare, finance, and education, you can acquire the skill of simply elucidating complex problems and build a knowledge base that leads to the next step. We also recommend exploring ensemble learning and the latest machine learning techniques after understanding decision trees. Now is the time to take a step into the world of data analysis. New insights and discoveries will surely have a significant impact on your career and daily life.

If you want to learn decision trees, we recommend this book (access here).

 

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