Decision trees are the predictive models or visual/analytical Decision Support Tools that utilize a tree-like model of decisions in which predictions are made on the ground of a series of decisions. It is a method of displaying an algorithm that just comprises conditional control statements.
A decision tree identifies your big decision as to the root, associated courses of action as the branches, and the possible outcomes as the leaves to let you have a birds-eye view of your decision-making procedure.
The most common use of decision trees occurs in operation research or decision analysis for identifying a strategy that can lead to the goal of accomplishment.
Decision trees are made through a flow-chart like structure whose-
- Internal node symbolizes a “test” on an attribute
- Each branch symbolizes the outcome of the test
- Each leaf node symbolizes a class label
- The paths from the root to leaf symbolizes classification rules
This post will take you deep into the world of decision trees and help you understand how they can alleviate your tasks of decisions making most conveniently and effectively possible. So, without any further ado, let us start with the introduction of this flow-chart like structures-
Decision-making is one of the toughest tasks in life.
We encounter robust points where we need to choose from the various options available. We need to make decisions for the betterment of us and the people around us.
In personal as well as in professional lives, we need to make such decisions. We need to choose the path we want to take and justify the same. In our personal lives, we can make decisions based on emotions and states of mind. However, this cannot be done in the professional sphere.
Here, the decisions need to be made based on a thorough knowledge of the topic and proper brainstorming with the rest of the colleagues. The process of decision-making can be made easier using decision trees.
For the professionals working in business management, public health, and health economics, concepts associated with decision trees, utility functions, influence diagrams, and other decision analysis tools are of great significance.
Due to their graphical structure, decision-trees are considered easy to understand and explain different business decisions, analytics, and operations. From predictive modeling to data exploration stages to understanding variable interactions, decision trees are of great use.
Let us dig deeper and define this tree structure of decision making more lucidly-
What Are Decision Trees?
Trees are symbolic of life. They have many implications and variations of meanings.
Even in decision analysis, the tree has an analogy. Reaching conclusions and making decisions can be achieved by representing situations in the format of a tree.
A decision tree is the diagrammatic representation of a decision-making process.
It maps out all the possible outcomes of a decision and then helps you choose the best path.
A decision tree generally starts with a single node. This node then splits into different branches, which represent various options available along with their consequences.
The consequences are weighed primarily based on probabilities, costs, and benefits.
Why use Decision Trees? Their Advantages
Decision trees make the process of making decisions pretty simple. It gives you the liberty of weighing different pros and cons and zeroing down on the best possible decision.
It is one of the essential qualities of a decision tree. You can consider various decisions and weigh the positive and negative points of each of these probable decisions. It helps you assess each aspect of every decision and every juncture in that process.
It helps the team not to confine themselves to a single process but explore all the possible avenues.
2.Alleviates the communication of complex processes:
Sometimes, it is hard to communicate intricate processes to your colleagues and team members. Textual reports may not always convey the decision-making process in a precise and concise manner.
However, if you represent the same decision in the format of a decision tree, it is far more readable and easily comprehendible. It helps in better communication and makes the process of decision-making more effective and easier.
3. Helps you go to the depths of every consequence:
The biggest mistake made by most people is that they do not think through before concluding. They do not weigh every pro and con and end up making the wrong decision.
A decision tree reduces the probability of such mistakes.
It helps you go to the depth of every solution and validate the right ideas. It also enables you to strike out the less effective ideas and do not let you stay in dilemmas for a long time. Working on decision trees centers around data and probability, not on the biases and emotions.
These trees are also highly effective in clarifying choices, objectives, risks, and gains. By using a well-structured tree, you will be able to flesh out productive ideas in the least possible time and resource.
Some of the most notable features of a good decision tree are-
Features of a Good Decision Tree
Some of the most notable features of a good tree are-
- A good tree to make the right decisions should always follow Occam’s razor.
- It should offer a high level of accuracy by using as few variables as possible.
- Best decision trees are easy to visualize and interpret.
- Good trees are the exception in making intuitive sense.
Let us now have a look upon different nodes that a good decision tree might comprise-
Types of Nodes
A decision tree has some nodes which show the checkpoints of a specific decision. Given below are some of the types of nodes.
1. Root Node
It is the topmost node of the decision tree. It is the most crucial node which represents the final decision needs to be taken. There is only one last root node in a decision tree.
2. Leaf Node
There can be more than one leaf node in a decision tree. The possible outcomes of the decisions to be taken are shown in a leaf node.
3. Square Leaf Node
It is a type of leaf node which shows the decision to be made. This decision has been weighed rightly, and one can take it with a positive mindset.
4. Circle Leaf Node
It is another type of leaf node, which is called a chance node. It represents the decisions which do not have fixed consequences. Since its outcome is unknown, you need to be prepared for facing adverse effects as well.
Now, you might be thinking about the steps that can help you make the best-suited tree for making the right choices. Let us have a look at the key steps here and now-
How to Draw a Decision Tree?
One can draw a decision tree using essential writing tools like a pen and paper or a whiteboard and marker. These days, there are specific computer tools that can help you draw decision trees.
Given below are a few easy steps to bring one.
Step 1: Start with the primary decision
You can draw a small square box to represent the initial decision. Then, you can draw lines from this box, which shows the possible outcomes of this decision.
Each line will bear the label stating the effect of this decision and what more you can do with them.
Step 2: Add chance nodes and decisions nodes
If the initial decision is a collection of some small decisions, you can represent them using these nodes. For the next choice, draw one more box. If the outcome of the decision is fixed, you can bring a square box.
Else, if the result is not set and there is any ambiguity, you can show that by a circular box.
Step 3: Go on expanding this tree until you reach an endpoint
You can keep this process going on till you reach the endpoint. Many people wonder what a parameter in a decision tree is.
The point after which there are no more choices left, and you have understood that the only way forward is that decision; you can declare that you have reached the end of the decision tree. You have to keep on repeating the second step until this point.
Role of Decision Trees in Data Mining and Machine Learning
Data mining and machine learning are the domains that encompass the projects that study dataset and predict the possible outcomes.
The automated predictive models can be built easily using these structures.
The optimal decision tree is the one that represents maximum data in the least number of nodes and levels. It helps in designing and making better systems.
The decision tree is a great concept and is used in many domains and arenas. It has also found its applications in machine learning and data mining domains due to its ability to use data to make favorable outcomes.
It is essential to keep the structure of your decision tree simple, plus opting for a professionally designed template for decision trees would also be beneficial for you.
The right use of these trees will, for sure, optimize your decision-making abilities.
Have you ever used such decision trees in making the right decisions?
Do you think these trees are effective in alleviating your decision making needs?