Decision tree title

Make complex decisions well-founded with decision tree

Janina Horn

If you regularly have to make important, complex decisions in your business life, the decision tree is an important tool.

It helps with decision making by modeling decisions in a tree structure, helping you make smart and informed decisions. 

In this blog article, we will take a closer look at decision trees, explore their application in various domains, and address the challenges and limitations associated with their use. 

We will also look ahead to future developments and trends that may impact the application of decision trees.

decision tree definition

Decision tree: definition

A decision tree is a graphical model that depicts decision-making processes and assists in choosing between different courses of action. It consists of nodes, branches and endpoints. Each node represents a decision, each branch point represents a decision point, and each end point represents an outcome. 

Decision trees are used in various fields such as marketing, financial analysis or human resource management. 

They help make decisions more objectively and effectively. 

Creating a decision tree requires a deep understanding of the decision process and the associated decision criteria. 

There are several methods and algorithms for creating decision trees, which will be discussed in more detail later in the article.


Advantages of a decision tree

Using a decision tree has several benefits for your business. The most important ones are:

  • Objective Decision Making: Decisions are made based on facts and data, not opinions or assumptions.
  • Effectiveness: Decision-making processes are carried out systematically, which saves time and resources.
  • Transparency: The decision logic is simple to understand and comprehensible.
  • Flexibility: Decision trees can be easily customized to reflect changes in the decision-making process.
  • Knowledge Management: Decision trees can document and share knowledge about the decision-making process.
  • Risk management: Decision trees make it possible to identify risks and make appropriate decisions.
  • Scalability: Decision trees can be applied to large data sets and complex decision processes.
  • Collaboration Support: Decision trees encourage collaboration and knowledge sharing between team members.
  • Automation: Decision trees can be generated and updated automatically, saving time and resources.
  • Effective communication: Decision trees can help make complex decision-making processes easier to understand and facilitate communication between stakeholders.

Possible applications in many different industries

The decision tree is a universal concept that you can apply no matter what industry you come from. 

This is also shown by the examples of possible applications of the decision tree for decisions in various industries and fields:

  • Marketing: Advertising strategy or pricing
  • Financial Analysis: Investments, lending or risk management
  • Human Resource Management: Recruitment of applicants or the evaluation of employee performance
  • Medicine: Diagnoses or treatment plans
  • Environment: Measures to reduce emissions or manage natural resources.
  • Education: Learning method or educational policy
  • Economy: Pricing, inventory management or production planning
  • Administration: Allocation of resources or the management of projects
  • IT: Selection of technology or the development of software
  • Law: Court proceedings or legal opinions

In principle, you can use a decision tree wherever complex decisions that cannot be made easily are required.

decision tree algorithms

Overview of the different methods and algorithms

To be able to use the decision tree, you should be at least roughly familiar with the different methods and algorithms in order to choose the most suitable one for you.

Below are the 8 methods that can be used when creating a decision tree.

ID3 algorithm

The ID3 algorithm is based on entropy, which measures the information content of the decision variables. 

It selects the decision variable that provides the most information to build the decision tree.

The ID3 algorithm is best suited for decision trees with discrete and homogeneous data. It is fast and easy to implement, but not as robust to noise and outliers as other algorithms.

C4.5 algorithm

The C4.5 algorithm is a further development of the ID3 algorithm, which can also handle missing data. 

It uses the information ratio method to select the best decision variable and creates decision trees with binary and multiple branches.

It is best suited for decision trees with heterogeneous data and can support binary and multiple branching.

CART algorithm

The CART algorithm can be used both Classification as well as Regression support and create decision trees with binary branches. However, it is prone to overfitting and can make inaccurate predictions if the data is not handled adequately.

It is based on the Gini index method, which measures the purity of the nodes in the decision tree.

CHAID algorithm

The CHAID algorithm is often used in the construction of decision trees with categorical data. 

It is based on the chi-square statistic, which measures the dependence between the outcome variable and the decision variables.

The CHAID algorithm is best suited for decision trees with categorical data. It can support binary and multiple branches and is robust to noise and outliers.

QUEST algorithm

The QUEST algorithm is particularly robust to noise and outliers. 

It uses a tree structure to estimate the probabilities of the target variable and creates decision trees with binary branches. It is thus best suited for decision trees with continuous and heterogeneous data.

MARS algorithm

The MARS algorithm can support linear and polynomial models in addition to decision trees. It uses splines to increase the prediction accuracy of the continuous target variable.

The MARS algorithm is best suited for decision trees with continuous data and can also support linear and polynomial models.

Random Forest

The Random Forest combines multiple decision trees to improve prediction accuracy. 

Each decision tree is created with a random sample of the data and the prediction of the Random Forest is determined by the average values of the predictions of each tree.

The Random Forest is best suited to improve prediction accuracy and reduce overfitting. It can be used for many different applications and is especially useful when processing large amounts of data.

Gradient boosting

Gradient boosting is another ensemble algorithm that also combines multiple decision trees, but can make particularly accurate predictions by iteratively optimizing the model. 

Gradient boosting uses gradient descent to minimize the residuals of the previous decision tree and build the next decision tree.

Gradient boosting is best suited to maximize prediction accuracy and can handle heterogeneous data. However, it requires more resources and can be prone to overfitting if not configured properly.

green with use case box

Use Cases

To give you an idea of how versatile decision trees can be, below are now 5 common use cases:

Customer analysis

One way for companies to figure out which customers are most likely to purchase their products and services is to use a decision tree. 

Various factors such as age, gender, income and interests are taken into account to make predictions. The decision tree thus offers an effective method for identifying the target group and can help companies target their marketing strategies more specifically at potential customers. 

By using a decision tree, the company can make the best use of its resources and increase its sales.

Risk assessment

A bank could use a decision tree to determine the risk of default on a loan. 

Various factors such as income, creditworthiness, length of employment and debt burden would have to be taken into account in order to ultimately decide whether or not a loan can be granted.

Disease diagnosis

A physician's help can be supported by a decision tree in diagnosing a disease.

Various factors, such as symptoms, age, gender or medical history, can be included to make informed predictions. 

The use of such a tree can provide valuable support to the physician and thus contribute to an improved treatment of the patient.

Marketing strategy

By using a decision tree, a company can draw conclusions about which marketing strategy is best suited to effectively sell a new product. 

Such a tree could take into account a variety of factors such as target audience, budget, marketing channels and product characteristics to predict which approach would be most effective. 

Thanks to this method, a company can make informed decisions and minimize the risk of bad investments or flops.

Fraud detection

An insurance company can use a decision tree to detect fraud early. 

This decision tree could take into account various factors, such as claim amount, claim type, insurance duration, and insurance history. 

Based on these factors, the insurance company can decide whether the claim is justified or whether it is an attempt at fraud. This method would allow the insurance company to detect possible fraud at an early stage and thus avoid losses.

Limits and challenges

Although decision trees are a powerful tool for predictive analytics, there are some challenges and limitations to their use that you should be aware of:

  1. Overfitting: Decision trees can be prone to overfitting, meaning they can become too complex and fit the data too closely, resulting in poor predictions on new data.
  2. Data quality: They are only as good as the data on which they are based. If the data quality is poor, the predictions of the decision tree can also be poor.
  3. Distortion: If the data are not representative, the decision tree may make biased predictions.
  4. Scalability: Decision trees can be difficult to implement on large data sets and may require more computing power.
  5. Transferability: They can only be created for specific applications and may not be transferable to other applications or data sets.
  6. Interpretability: The more complex the decision tree becomes, the more difficult it can be to understand and interpret the model's decisions.
  7. Choice of algorithm: Choosing the right algorithm and parameters can be difficult and may require experience and expertise.
  8. Categorization: Decision trees are better suited for categorical data and may have difficulty handling continuous data.

Conclusion - Future developments and trends

Overall, the decision tree offers many advantages as a tool for decision making and is used in various fields due to its simplicity and flexibility. 

However, future developments and trends may enable even better use of this tool:

  • For example, additional algorithms and methods can improve the predictive accuracy of decision trees. 
  • Integrating decision trees with other technologies such as artificial intelligence and machine learning can also create new applications. 
  • Another future development could be to implement decision trees in real-time systems to make fast decisions in real time. 

In summary, the decision tree already offers many advantages and will continue to play an important role in decision making in various areas.

Are you still interested in further input? Then the following articles might be something for you:

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