What are Decision Trees? How Do They Influence Business Decisions?

Businesses have to deal with a range of uncertainties. However, they should still make the right decisions to maintain profitability.

 

However, that may become a challenge if there is no formal structure or process to make informed decisions.

 

As a result, businesses rely on different methods and models to drive effective decisions. In this regard, decision trees can be a suitable tool for making classifications and predictions.

 

In this blog, we will discuss decision trees in detail and explore their pros and cons. You will also learn how the method is used to make meaningful decisions.

 

What is a Decision Tree?

 

A decision tree is a tree structure based on the flowchart. It follows a sequential order to help businesses predict future possibilities.

 

In a decision tree, an internal node represents a test for an attribute. Additionally, every branch on the tree denotes a possible result of the test. Moreover, every terminal node contains a class level.

 

Decision trees are excellent as learning methods. You can develop a model that allows you to predict the outcome of your target variable. The tree can be used to learn decision rules from the data you provide.

 

Businesses can use this model to predict future outcomes by weighing all possibilities. Most importantly, decision trees are helpful in data mining to zero in on a strategy to achieve a goal. Even machine learning models use decision trees to make accurate predictions.

 

Example of Making Prediction with Decision Tree

 

Let’s take an example to illustrate how decision trees work. We will predict how the weather is going to be to determine if it is suitable to play tennis.

 

 

The first nodes contain the possible status where the weather can be sunny, overcast, or rainy. If the weather is sunny, we need to check out the humidity level, which can be high or normal. We can also find out if the humidity level will be high or normal.

 

Next, the game may be suspended if the sky is overcast. There is a single outcome for this variable.

 

Lastly, we may predict the wind conditions if the weather is rainy. We can find out if the wind will be strong or weak.

 

Based on these predictions, we can decide if it is right to play tennis outside.

 

However, businesses deal with more complex decision trees with several nodes and branches. It is possible to use data tools to make the right predictions with different possibilities.

 

Here is an example:

 

 

How to Make a Decision Tree

 

A decision tree has three main parts:

 

Root node: The first node or the primary node on the decision tree denotes the decision you want to make.

 

Branches: The first node gives way to branches that contain different courses of action. You can rely on these actions while making your decision. Branches are generally represented with an arrow line.

 

Leaf node: Leaf nodes are placed at the posterior of the branches. They denote the possible outcomes of the model. We can use square leaf nodes for making additional decisions. Consequently, we can rely on round leaf nodes to represent unknown outcomes.

 

Below are the steps you can follow to construct your decision tree:

 

●      Draw your first node as a rectangle and write the main idea or decision to make

●      Now, add the branches to your tree and account for every possible course of action

●      Draw the leaf nodes after your branches and put in your criteria or questions

 

You may continue to add as many branches as you need from the leaf nodes to accommodate every possibility. The decision tree will be complete when you have accommodated all criteria, resolved them, and reached your outcome.

 

Advantages of Decision Trees

 

Let’s take a look at some of the advantages of using a decision tree.

 

Simple and Quick

 

Decision trees are a visual and simple way to understand and interpret data. You also need very little data preparation and can work without data normalization or blank values.

 

Handles a Range of Data

 

A decision tree is suitable for working with categorical and numerical data. Moreover, you can use it to handle dimensional data.

 

Best of all, you can use it for multi-output problems.

 

High Flexibility

 

You can enjoy a higher degree of flexibility with decision trees as they are non-linear. They can help you account for and predict numerous possible outcomes to make more informed decisions.

 

For example, a student counselor may use a decision tree to help learners decide on their future career paths. They can account for specific factors of the student, like interests and traits.

 

No Bias or Prejudice

 

A business decision may involve several stakeholders. However, the more stakeholders you have, the more are the chances of emotions or biases affecting the final decision.

 

Fortunately, decision trees are free from any prejudice or bias. They account for all risks and incentives and provide a balanced approach to making decisions.

 

Disadvantages of Decision Trees

 

Decision trees have a few drawbacks, which we will explore below:

 

Overfitting

 

Decision trees can become too complex to generalize data efficiently. This situation is called overfitting and requires techniques like pruning to resolve the issue.

 

Not Suitable for All Decisions

 

It may not be possible to use decision trees to learn all concepts. They are not able to express all concepts in a simple manner, such as multiplexer or parity problems.

 

Instability

 

Decision trees sometimes may not be stable enough to provide accurate results. A minute change in data may give rise to a whole new different tree.

 

Therefore, you may need to use your decision tree inside an ensemble.

 

Final Thoughts

 

Decision trees are an excellent tool to evaluate possible outcomes based on available data. It is also a learning model and is used in data mining. Decision trees can help businesses make accurate predictions to drive better decisions and profitability. They can also be used for a range of purposes, like making decisions on a personal level. However, you need to be aware of the drawbacks of decision trees to create an effective model.