What is a decision tree? Definition, analysis and example of DECISION TREE

Decision Tree is one of the most important and widely used tools in the field of data mining and data analysis. This tool helps you to extract patterns and algorithms that help you make better decisions from the large and complex data set that you have, in an orderly and structured manner. Using the decision tree, you can organize the data into Break into smaller groups and identify different patterns within them. This tool allows you to make the best use of your data and make more informed decisions. Decision trees, used in both marketing and machine learning, can help you choose the right path. Decision trees are commonly used in business to analyze customer data and make marketing decisions, but they can also be used in fields such as medicine, finance, and machine learning. In this article from Avir’s artificial intelligence website, we are going to do a complete review of the decision tree. Stay with us until the end.

What is a decision tree?

A decision tree is a flowchart that plots all the potential solutions to a given problem. These tools are commonly used by organizations to help determine the most optimal course of action by comparing all possible outcomes of a set of decisions.

For example, having a decision tree can be used to help a company decide which city to move its headquarters to, or whether to open a new, modern office. Decision trees are also a popular tool in machine learning because they can be used to build predictive models. This type of decision tree can be used to make basic predictions, such as whether or not a customer will buy a product based on their previous purchase history. Using this decision tree application is essential for online marketing and store websites.

Decision tree structure

The structure of decision trees includes the following:

• Root Node: The root node shows the whole community or instance. Then it is divided into two or more homogeneous sets.
• Splitting: The splitting process involves separating a node into several sub-nodes.
• Decision Node: A sub-node becomes a decision node when it is divided into more sub-nodes.
• Leaf or terminal nodes: Nodes that do not divide are called leaf or terminal nodes.
• Pruning: Pruning is the process of removing sub-nodes from a decision node. It can be described as reverse division.
• Branch or Sub-Tree: A branch or sub-tree is a division of the overall decision tree.
• Parent and Child Node: A node that is divided into sub-nodes is called a parent node. Child nodes are children of a parent node.

Applications of decision tree

A decision tree is usually suitable for problems with the following characteristics:

1. Examples represented by attribute-value factor pairs

Instances have fixed sets of attributes and their values. These trees help make decisions with a limited number of possible discrete values ​​and allow the numerical representation of real-valued attributes such as level or degree.

2. Objective functions with discrete output values

The decision tree gives the possibility to categorize questions whose answer is yes or no and functions with more than two possible output values ​​and outputs with real values.

3. Separate descriptions

A decision tree is useful in representing discrete statements.

4. Data with unknown characteristic values

Decision tree method helps to make decisions even with missing or unknown values.

In real-world applications, decision trees are useful in both business investment decisions and general individual decision-making processes. Decision trees are very popular as prediction models when making observations. Furthermore, decision tree learning is a supervised learning approach used in statistics, data mining, and machine learning.

A step-by-step guide to creating a decision tree

You can use software tools or online collaboration platforms to create a decision tree, but all you really need is a whiteboard or a pen and paper.

Draw the initial node. This square node represents the main decision you want to make. For each possible action you can take at this stage, draw a branch and label it with the name of that action. You can include additional information such as the financial cost of making that decision here.

Add knots to the ends of each branch. Then consider what would happen in each labeled scenario. Would following that course of action lead to a different decision? If so, add another square and repeat the process. If the decision leads to a random outcome, draw a circular node and try to determine the possible outcomes and the probability of each occurring.

Expand the tree to reach all endpoints. Continue adding decision nodes, chance nodes, and branches until you have no more choices. Then cover each branch with a result node. This result node describes the end result of following that path and must contain some sort of value or score to be able to compare between each endpoint.

An example of a decision tree

Let’s look at an example for a better understanding:

A person decides to invest a certain amount of his money. As a result, he considers three options: mutual funds, bond funds and cryptocurrencies. He analyzes them according to a criterion that is a priority for him – these options must have a return of more than 60%. He knows that the risk associated with it is also high, but the amount he invests is extra money that does not harm his original capital. Since only cryptocurrencies can generate such returns, he chooses to invest in buying cryptocurrencies.

Check out the decision process image below.

Advantages and disadvantages of decision tree

In the following, we will examine the main advantages and disadvantages of using a decision tree.

Advantages of decision trees

• With the possibility of visual interpretation of data, it helps to make easy decisions.
• The decision tree structure can be used for a combination of numerical and non-numerical data.
• Decision tree classification enables decision making by categorizing them based on specifications.

Disadvantages of decision trees

• If the tree structure is complicated, unreadable data is obtained from it.
• Computations in predictive analytics can be tedious, especially when a decision path involves multiple chance variables.
• A slight change in the data can significantly affect the structure of the decision tree and give a different result than it would in a normal environment.

Difference between decision tree and random forest and logistic regression

• A decision tree is a structure where each vertex of the figure is a question and each branch descending from that vertex is a potential answer to that question.
• A random forest combines the output of different decision trees to produce a single result. Therefore, it solves classification and regression problems. This method is simple.
• Logistic regression calculates the probability of a certain event occurring based on a set of independent variables and a given data set. The range of the dependent variable in this method is 0 to 1.
• Although decision tree, random forest and logistic regression are all concerned with reaching the same result based on probability, they are different.

The role of decision trees in data science

We’ve mostly focused on using decision trees in choosing the most effective course of action in business, but this type of information mapping also has practical applications in data mining and machine learning.

In this context, decision trees are not used to manually determine some optimal action, but rather as a predictive model to make automatic observations about a data set. These algorithms take in huge amounts of information and use a decision tree to derive accurate predictions about new data points. For example, consider using medical data from thousands of hospital patients to predict a person’s likelihood of developing a disease.

Frequently asked questions about decision trees

• What is a decision tree in machine learning?

Decision tree learning is supervised machine learning where the training data is continuously segmented based on a specific topic. It produces the corresponding output for a given input such as training data.

• What is entropy in a decision tree?

Entropy controls how the decision tree decides to split the data. Information entropy measures the level of surprise (or uncertainty) in the value of a random variable. In simpler terms, entropy is a measure of homogeneity.

• How does a decision tree work?

The decision making process is done by branching from the nodes starting from the root node. Branching nodes represent different possibilities where the user decides to select or discard that option based on preferences. Results or conclusion nodes are called leaves.

• What is decision tree analysis?

Decision tree analysis is weighing the pros and cons of decisions and selecting the best option from a tree-like structure. This process includes data assimilation, decision tree classification and selection of the best available option.