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Binary Decision Tree

What is a decision tree?

It's a tree diagram prepared while doing risk analysis, presenting all decisions and the potential consequences of those decisions. Simply put, it helps in finding out what is the easiest, or the most straightforward, or the cheapest way to achieve a decided goal. In this diagram, potential decisions are represented by squares, possible consequences are represented by branching off a circle from an appropriate square, and potential end results are represented by triangles. In simple words, it is a descriptive means of calculating conditional probabilities of possible outcomes in terms of reaching the goal.

What is a binary decision tree?

Like all other binary data structure, binary decision tree is used to represent a boolean function. It is a rooted acyclic graph in which every node is a decision node and each decision node has two child nodes called high child (value 1) and low child (value 0).

Business decision making with decision trees

Almost all business owners have to make a decision in the face of uncertainty and face with the consequences of those decisions. But it doesn't mean that such decision have to be made in the dark. By carefully analyzing the outcomes of options or alternatives available to you before making any decision can go a long way in determining whether you will make profit or loss. It can help a business owner in keeping themselves aware of options they are left with and which decision is going to be least painful for them.  It helps in making up our minds in what sort of actions should be taken in future which can yield best results.

A decision tree, as mentioned above, is a graphical representation of which path was taken by the business owner and which path was not taken. It represents how the decision taken by you to choose one path over the other has affected your business. Another example of making use of binary decision tree in business is experimental drug development. If you are drug manufacturer who is trying to find a cure for a disease, and you can make profit of hundreds of millions if you are successful in it. Since you may not predict everything with accuracy, a binary decision can help you evaluate probabilities that can help you making a decision, whether or not to go for it.

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