Decision-making is an important skill for every professional. During your business career, you need to make choices that can have many results. Decision trees or decision trees can guide you to logical answers for small and large questions by setting the possibility of conclusions from several choices.

In this article, we will discuss the DECISION Tree, explore the use of decision trees, and how to make it.

### What is a DECISION TREE?

Decision Tree is a support tool with structures such as trees that model possible results, resource costs, utilities, and possible consequences.

Decision Tree provides a way to present algorithms with conditional control statements. They include branches that represent decision-making steps that can lead to profitable results.

The flowchart structure includes an internal node that represents a test or attribute at each stage. Each branch represents the results for attributes, while the path of the leaves to the roots represents the rules for the classification.

Decision Tree is one of the best forms of learning algorithm based on various learning methods.

They enhance predictive models with accuracy, convenience in interpretation, and stability. This tool is also effective in adjusting non-linear relations because it can solve the challenges of data adjustments, such as regression and classification.

Called a decision tree or decision tree because the choice branches, forming a structure that looks like a tree.

You can make vertical or horizontal decision trees depending on your preferences. Read the horizontal decision tree from left to right and the vertical decision tree from top to bottom.

Tree decision works best when you follow basic flow diagram rules:

• Rectangle or square cage: Show the beginning of the tree where you write a question.
• Line: represents tree branches. These are all possible actions.
• Circle: indicates uncertain results that you will need additional branches for clarification.
• Triangle: Give clear and final answers. They are also called “leaves.”

A biting decision tree allows you to visualize the results of each choice in an organized way.

You can use a decision tree when you have a specific purpose, like determining whether you should accept job offers.

This tool is also useful if you need to evaluate a large number of data or statistics. For example, if you are a sales agent and want to determine how much income can be produced by prospective customers versus the cost of pursuing and maintaining relationships, you can use the decision tree to analyze the return on investment.

### Decision type

There are two main types of decision trees based on target variables, namely categorical variable decision trees, and continuous variable decision trees.

#### 1. Decision tree variable category

A category variable decision tree includes a categorical target variable which is divided into categories. For example, the category can or not. The category means that each stage of the decision process falls into one of the categories, and does not exist.

#### 2. DECISION TREE continuous variables

The continuous variable decision tree is a decision tree with a continuous target variable. For example, individual income whose income is unknown can be predicted based on available information such as work, age, and other continuous variables.

### Examples of Application of Decision Tree

#### 1. Assess prospective growth opportunities

One of the Applications of the Decision Tree involves evaluating prospective growth opportunities for businesses based on historical data.

Historical data on sales can be used in decision trees that can cause radical changes in business strategies to help expand and grow.

#### 2. Using demographic data to find prospective clients

Another application of this tool is in the use of demographic data to find prospective clients. They can help streamline the marketing budget and in making the right decisions about the target market that is the business focus.

In the absence of decision trees, businesses can spend market marketing without considering certain demographics, which will affect their overall income.

#### 3. Serves as a support tool in several fields

Lenders also use decision trees to predict the possibility of customers failing loans, by implementing the manufacture of predictive models using client’s past data.

The use of support trees supporters can help lenders in evaluating customer credit feasibility to prevent losses.

This tool can also be used in operating research in logistics planning and strategic management. They can help in determining the right strategy that will help the company achieve the desired goals.

Other fields where decision trees can be applied including techniques, education, law, business, health, and finance.

### How to Make Decision Tree

You can follow these steps when making a decision tree:

Draw a rectangle, and write your question or idea in it. If you want to make horizontal trees, your rectangular image is on the left side of the page so you have room to draw a line.

For vertical trees, drawing boxes at the top of the page and lower them. For example, if you want to determine whether you have to ask for a salary increase, you can draw a rectangle at the top of the page and write, “Request a salary increase?” inside it.

Draw as many lines as you need from the box to determine the action. To continue for an example, you can draw two branches under your rectangle and label “Yes” and “no.” This indicates that you request or not ask for a salary increase.

#### 3. Add a decision node to the branch

The circle shows that the results of the branch are unclear and you need to ask more questions. The triangle shows that the results are almost certain.

For example, you can add a circle at the end of the branch “yes” and “no”. In the “Yes” circle, you can write “Get a salary increase?” To determine whether your manager will give you a salary increase, and in the “no” circle, you can write, “Get a salary increase in the future?” To determine whether you are sure to get a salary increase without asking in the future.

#### 4. Continue as needed

Continue your decision tree until you check all possible results and can make the right decision. In the example, you will continue until you reach the answer whether you have to ask for a salary increase.

### Tips in Making Decision Tree

Consider the following tip to make effective decision trees:

• Your tree color code. Give color codes on your branch and knot to identify results easily. For example, you can make your initial idea to be green and yellow, blue and purple nodes to distinguish each. Use the color scheme to make it interesting visually.
• Use the flow diagram symbol. If you make a decision tree to be shared with your team or manager, the standard groove diagram symbol ensures that your tree is easy to understand by many viewers.
• Make your symbol the same size. When drawing your symbol, try making it the same size. This will help you provide the same value to each and make trees easier to read.
• Use templates. There are many online templates that you can use to make your tree look simple. Some also have mathematical functions if you use trees to handle data and statistics.
• Know when to use a decision tree. The decision tree works best when you have special goals and need to see results for each choice you can make. Because it is difficult to determine the results of the original idea, you must use a decision tree when you can predict the answer safely.

### Advantages of using Decision Tree

#### 1. Easy to read and interpreted

One of the advantages of the decision tree is the output is easy to read and interpreted, even without requiring statistical knowledge.

For example, when using a decision tree to present demographic information on customers, the marketing department staff can read and interpret the graphical representation of the data without requiring statistical knowledge.

Data can also be used to produce important insights on probabilities, costs, and alternatives for various strategies formulated by the marketing department.

#### 2. Easy prepared

Compared to other decision techniques, decision trees need a little effort to prepare data. Users, however, need to have information that is ready to create a new variable with the power to predict the target variable.

They can also make data classification without having to calculate complicated calculations. For complex situations, users can combine decision trees with other methods.

#### 3. Less data cleaning is needed

Another advantage of the decision tree is after the variable is created, less data cleaning is needed. The case of lost value and outlier is less significant in the decision tree data.

#### 1. Unstable properties

One limitation of the decision tree is that they are mostly unstable compared to other decision predictors.

Small changes in data can produce major changes in the decision tree structure, which can deliver different results from what users will get in normal events. The results of the resulting results can be managed by the machine learning algorithm, such as boosting and bagging.

#### 2. Less effective in predicting results from continuous variables

In addition, decision trees are less effective in making predictions when the main goal is to predict the results of continuous variables. This is because the decision tree tends to lose information when categorizing variables into several categories.

### Conclusion

• DECISION TREE is used to handle non-linear data sets effectively.
• DECISION TREE is used in real life in many fields, such as techniques, civilian planning, law, and business.
• Decision trees can be divided into two types; Category variables and continuous variable decisions.

That is a complete discussion of decision trees which will make it easier for you to make decisions in business based on data.