Machine learning is becoming more and more sophisticated. So much so that it can help with decision making too. A decision tree is essentially a layout of various outcomes associated with a series of choices related to each other. Organizations and individuals can utilize it to weight their actions based on multiple factors such as benefits, probabilities, and costs. You can use a decision tree in python for mapping out algorithms to predict the most favorable choice or to drive non-formal discussions.

Data miners use this tool quite frequently to derive strategies for reaching various goals. However, you will find that machine learning is where the use of a decision tree is more prevalent. Typically, a decision tree begins with one node. It can branch into numerous outcomes. Every result leads to added nodes that branch into more possibilities, giving it a shape similar to a tree.

What are the Different Nodes of a Decision Tree?

A decision tree has three node types: decision nodes, end nodes, and chance nodes. Chance nodes represent a circle – it highlights the probabilities of a particular result. The square shape represents the decision node – it indicates a choice that you have to make. Finally, the end node represents a decision’s outcome.

Analysis Example of a Decision Tree

You can reduce risks and maximize the chances of achieving desirable outcomes by calculating the predicted value or utility of every choice on the tree. If you want to compute a choice’s expected utility, subtract that decisions cost from its expected benefits. Expected benefits are proportional to the overall value of every outcome that could occur from that option.

When you are trying to find a desirable outcome, it is essential to consider the decision maker’s preferences regarding utility. For example, some are ready to take risks to gain sizeable benefits, while others want to take the least amount of risks.

So, when you utilize your decision tree with its probability model, it can come in handy to compute an event’s conditional probability. It could also be ideal for determining whether it will happen based on other events. Therefore, you must begin with an initial even and follow its path to the event you are targeting. Then, multiply each event’s probability together to get the results.

In cases like these, you can use a decision tree in the form of a conventional tree diagram that maps the probabilities of various events, such as rolling the dice twice.

Understanding the Decision Tree Algorithm

The algorithm of a decision tree in python belongs to a group of supervised algorithms. Also, unlike most supervised learning algorithms, you can use the algorithm of a decision tree to solve classification and regression problems.

Once again, a decision tree’s primary goal to develop a training model is to predict the value or class of a target by understanding fundamental decision rules taken from older data, which programmers also refer to as training data.

Begin with the tree’s root when you are trying to predict a record’s class label and compare the root attribute’s value with the characteristic of the record. When it comes to comparison, follow the branch that corresponds to its value, after which you can go to the other node.

How Many Types of Decision Trees are there?

Decision tree types depend upon target variables. There are two types of decision trees:

• Continuous Variable Decision Tree
• Categorical Variable Decision Tree

For instance, we have to predict if someone will repay for their renewal premium through their insurance company. What we know in this scenario is that the customer’s income is a massive variable.

However, the insurance service does not possess all of their customer’s details. Most of you will know that this variable is critical. Therefore, we can then develop a decision tree for predicting a customer’s income through other variables like products and occupation. We will mostly be speculating values for continuous variables.

What are the Pros and Cons of a Decision Tree?

The Strengths

• Decision trees offer a clear idea of the critical fields for classification or prediction
• A decision tree is capable of handling categorical and continuous variables
• They do not require excess computation for performing classifications
• These trees can generate easily understandable rules

The Weaknesses

• Errors are quite common in decision trees, particularly when it comes to classification problems and training examples
• Decision trees are not an ideal option if you are creating estimation tasks for predicting a continuous attribute’s value
• Training a decision tree can be quite computationally costly. You have to sort the spitting field of each node’s candidate to determine the most favorable split. Some algorithms utilize combinations that require a comprehensive search to determine suitable combining weights.
• Pruning the algorithms is quite costly, mainly because you have to compare and form the sub-trees.

Essential Decision Tree Terminologies

Child and Parent Nodes

Any node that divides to sub-nodes is also known as a parent node. Sub nodes, on the other hand, are the child nodes.

Sub-Tree/Branch

A decision tree’s section subsection is its sub-tree or branch.

Pruning

Pruning is the process where you reduce the decision tree’s size by plucking away its nodes.

Terminal Node/Leaf

Leaf or Terminal nodes do not have children and do not go through extra splits.

Decision Node

When a single sub-node divides into multiple nodes, it becomes a decision node.

Splitting

Splitting is the process that divides one node into multiple sub-nodes.

Root Node

The root node represents the overall sample or population of each node. It further divides into multiple homogenous sets.

Final Thoughts

Developing a decision tree in python can solve multiple decision-related issues for large and smaller organizations. It can also help individuals decide whether the choice they are about to make would be profitable. Developers often utilize python’s sclearn library to develop a sclearn decision tree. Its implementation and algorithm are more efficient and yield better results.