What is a decision tree?
A decision tree is a machine learning algorithm that classifies or predicts outcomes based on a set of characteristics. In a tree structure, each node represents an attribute (or trait), each branch represents a decision based on that attribute, and each leaf represents a final result.
The tree-building process starts at the root and proceeds to subdivide the data into subgroups based on attributes to achieve the most homogeneous subgroups possible. This means that elements in the same subgroup will have similar results. Decision trees are commonly used in various fields such as finance, healthcare, and marketing to support the decision-making process.
Types of Decision Trees
Each type of decision tree has its characteristics, advantages, and disadvantages, depending on how they process data and the division criteria. Below, we’ll explore some common decision tree types and learn how they work to solve different data analysis problems.
ID3
ID3 (Iterative Dichotomiser 3) is a decision tree algorithm based on information theory. It uses entropy and gains information to choose which attributes divide the data at each node. This process is repeated until leaves are reached or there are no more attributes to divide. ID3 is suitable for small data sets and does not handle continuous data or missing values well.
C4.5
C4.5 is an improvement of ID3, developed by Ross Quinlan. C4.5 can handle continuous data by creating breakpoints, handling missing values, and allowing this model pruning to reduce complexity. In addition, C4.5 also uses an information gain ratio to select attributes, helping to overcome some limitations of ID3, such as the tendency to select attributes with more values.
CART
CART (Classification and Regression Trees) is a decision tree algorithm used for both classification and regression. Different from ID3 and C4.5, CART uses the Gini index to evaluate the homogeneity of subgroups. It builds a binary tree, meaning each node has only two child branches. CART also supports tree pruning to avoid overfitting and can generate prediction trees for both categorical and numerical variables.
Decision tree symbols and meaning
When constructing and analyzing this model, it is essential to understand the symbols and their meanings. Below, we will learn about these symbols and their meaning in building an effective decision model.
Root Node
Root Node is the first node in the decision tree, where the classification or prediction process begins. This node contains all the original data and is split based on the attribute that provides the highest information value. The root node plays an important role in determining the direction of the tree.
Branches
Branches are links between nodes in a decision tree. Each branch represents a decision or condition based on the value of an attribute. They represent possible choices from one node, leading to subsequent nodes or leaf nodes. Branches help keep track of the different paths data can take based on set criteria.
Leaf Node
Leaf nodes are the last nodes in the decision tree and no longer split further. Each leaf node represents a classification label or a specific predicted value, the final result of classification processes based on previous attributes. Leaf nodes provide a specific output after the data has been processed through the entire decision tree.
How to draw a decision tree
A systematic process is needed to create a model that classifies or predicts the data to draw a model. Below, we’ll explore each step in the decision tree drawing process in detail to better understand how to create an effective model.
Determine decision goals
The first step in drawing this model is to identify the goals you want to achieve. This could be classifying groups of objects, predicting specific outcomes, or optimizing a process. This goal will guide data selection and evaluation criteria during the tree building.
Collect data and determine decision criteria
Collect data related to the problem to be solved. Then, identify the important attributes or criteria that will be used to separate the data. These criteria must be meaningful and strong enough to influence the final decision.
Select root attributes
Based on the determined criteria, select the root attribute – the first attribute with the highest distinguishing power. This is the most important attribute to divide data into different groups. Choosing the right root attribute will help create a solid foundation for the tree structure.
Divide into child branches
The parent attribute divides data into child branches. Each branch represents a different decision or outcome based on the value or range of the parent attribute. This process will help shape the structure of the decision tree, with each branch leading to different child nodes.
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Repeat process for child nodes
Continue applying the attribute selection and branching process to each child node, as done for the root node. Each child node will become the root node of the new subtree, and the choice of attributes to split will be based on the remaining data at each node.
Determine final decision at leaf nodes
When the division process cannot continue (because the data is no longer distinguishable or has reached the maximum depth), the last nodes will eventually become a leaf node. Each leaf node represents a final, clearly defined result or decision for each specific data case.
How to choose the best attribute at each node
When building this model, choosing the best attribute at each node is an important step to ensure the model works effectively. Below, we’ll explore common methods to understand how to select the best attribute for each node in a decision tree.
Information Gain
Information Gain is an index used to select attributes at each node in the decision tree. The resulting information measures the degree of reduction in uncertainty (entropy) after dividing the data based on that attribute. The attribute with the highest information gain will be the best choice because it reduces uncertainty the most.
Gini Index
The Gini Index is a criterion for evaluating the homogeneity of data groups after division. The Gini index calculates the degree of impurity in data groups, with lower values indicating greater homogeneity. The attribute with the lowest Gini index is the best choice, as it creates more homogeneous groups.
Advantages of Decision Trees
Decision trees bring many benefits to data analysis and machine learning models. Below, we will explore in detail the key advantages of decision trees and why they are popular in many data analysis applications.
Easy to understand and interpret
A decision tree has a clear structure with nodes representing attributes and branches representing decisions based on those attributes. This facilitates visualization and explanation of the decision-making process. Users can easily track and understand how decisions are made, helping to increase trust and transparency in predictions or classifications.
No data normalization required
One of the significant advantages of this model is that they do not require data to be normalized before entering the model. This reduces the effort and time needed to prepare data, as this model can work directly with the data in its raw form and are not affected by different units of measurement or different ranges of each other’s characteristics.
Implicit feature selection
Decision trees automatically select and classify important attributes during model-building. The decision tree eliminates unimportant or redundant characteristics without performing a separate feature selection step by assigning the attributes with the highest separating power to the dividing nodes. This helps reduce model complexity and focus on critical elements.
Disadvantages of Decision Trees
Although decision trees have many advantages, they also have some significant disadvantages. Below, we’ll look at the main drawbacks of this model and how they can affect the model’s predictive and analytical capabilities.
Prone to Overfitting
Decision trees have a high risk of overfitting, meaning the model overfits the training data and has poor generalization ability when encountering new data. This happens when the tree is too deep and complex, leading the model to learn too many small details of the training data, reducing performance on unseen data.
Sensitive to Data Fluctuation
his model can be strongly affected by fluctuations in the training data. Small changes in the data can lead to the generation of different tree structures, which can reduce the stability and prediction accuracy of the model.
Not Globally Optimized
Decision trees often only optimize decisions at the local level, that is, at each node, without considering the overall structure of the tree. Therefore, the tree may not achieve a globally optimal structure, leading to submaximal performance on complex data sets.
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Poor Performance with Large Data Sets
With vast and complex data sets, this model can become too deep and complex, reducing performance and increasing computation time. Building and maintaining a large decision tree can make it difficult to process and analyze data effectively.
Websites that provide decision tree templates
Using ready-made templates can save time and effort when designing and presenting a decision tree. Below are some useful websites that provide decision tree templates to start quickly and effectively.
Canva
Canva offers many customizable decision tree templates that are beautiful and easy to use. You can design a decision tree by dragging and dropping elements, adding icons, and changing colors to your liking. Canva’s diverse library of templates is great for professionals and students, helping to create professional decision trees quickly.
https://www.canva.com/graphs/decision-trees/
Miro
Miro provides an online decision tree template, allowing you and your team to collaborate in real-time. This template helps filter through multiple ways to solve problems and identify potential obstacles. You can edit the template as you like, adding different branches and outcomes to find the best solution for your situation.
https://miro.com/templates/decision-tree/
Smartdraw
SmartDraw provides an online decision tree generator with simple commands and intelligent formatting. You can create automated decision trees from data, integrating with applications like Microsoft Office, Google Workspace, and Atlassian. SmartDraw allows saving directly to existing storage systems and sharing decision trees with anyone, including those without SmartDraw software.
https://www.smartdraw.com/decision-tree/examples/
Decision trees are essential in analyzing and predicting information thanks to their intuitive and accessible structure. Mastering how to build and optimize decision trees will help you apply this tool effectively in many practical situations. Hope the information Replus brings will be helpful to you!
