Why is decision tree analysis is vital for your business?
“The possible solution to a given problem emerging as leaves of a tree, each node representing a point of deliberation and decision” – Niklaus Wirth, Programming Language designer
A Decision tree is a graphical depiction of all the possible outcomes to solve a specific issue and a visual representation of the most favourable outcome from among the alternatives. Decision tree analysis is done by answering certain affirmative and negative questions until a final decision can be made.
It is a scientific model that helps choose the most favourable decision from among several alternatives after carefully considering the risks and benefits. It is a schematic representation followed by different decision and their probability of occurrences. The decision tree is useful for assessing the ‘what if’ thought process.
This analysis is represented by lines, squares and circles. The squares are decisions, lines the consequences and the circles are the uncertain outcome. The very first node from which the split begins is called as the root node. The final decision node which does not split any further is called a leaf node. The very first step in decision tree is to select a best attribute. Best attribute is decided by Entropy and Information Gain. Entropy measures the uncertainty present in the data and information gain indicates how much information a particular variable/feature gives about the final outcome.
Decision trees are very useful in data analysis as they break down complex problems into chewable parts. They are highly utilised in prediction, data classification and regression. It is a game of plotting between a choice, chance and outcomes. They cover a wide range of businesses like Healthcare, Finance, Technology, Law, Engineering, etc.
Application of Decision Tree
1. Spotting growth opportunities – Past data can be used to predict future prospective opportunities. They spell out methodologies that can be implemented or strategized to help in growth
2. Finding prospective clientele – Target markets can be spotted and identified using decision tree analysis. This will help the company focus their resources in implementing suitable strategies for the said market
3. Works as a support mechanism – The predictive model can be used to evaluate the credit worthiness of clients based on past data. They can also help in operations management and logistics.
Besides these, decision tree is also used widely in sentiment analysis, fault diagnosis, variable selection, missing value handling and data manipulation.
Decision trees portray the realism in situations as they come with possible chance outcomes. They are simple to understand because of the pictorial illustration they bring to data. But in reality most of our everyday decisions are based on plotting a tree in our heads. We constantly weigh the option of a possible outcome before proceeding with the optimistic, most likely or pessimistic scenario. Believe it or not!!!!
“Be decisive, a wrong decision is generally less disastrous than indecision” – Bernhard Langer
Authors – Benila Jacob, Mark John