We all have seen trees, in the case of trees there is one tree but there are many branches of trees which go in different directions, in the case of machine learning and data mining similar tool is used which is called decision tree analysis. A decision tree analysis in simple words refers to that analysis where an individual can visually organize and make decisions by breaking down options based on specific conditions. Hence one can compare it with a flowchart just like a flowchart has different boxes where the selection of each box gives different outcomes. In order to get a better understating about this concept one should look at some of the important features of decision tree analysis –
Characteristics of Decision Tree Analysis
Easy to Understand
The first and foremost feature of decision tree analysis is that it is very easy to understand even for those people who do not have any technical background or expertise. As the saying goes a picture speaks a thousand words the same thing applies to decision tree analysis because if you give 10-page complex data to anyone then chances are he or she may not even bother to read all 10 pages but if you present the same set of data through flowchart than they will be intrigued and take interest in the decision tree.
Tree Structure
Another important feature of decision tree analysis is that the decision trees are organized into a tree-like structure, with a root node representing the initial decision or question, and branches representing the possible outcomes about the question or possible outcomes of the decision under consideration by the company.
Descriptive and Predictive Modelling
Decision tree analysis can be used to determine which factors are most crucial for describing the target variable as part of descriptive modelling, which entails identifying the relationships between various variables in the data. Decision tree analysis can also be used for Predictive modelling which involves using the information from the data to make predictions about new observations. Hence one can say that decision tree analysis can be used for current data as well as for predicting future variables based on current data
Handling Missing Values and Categorical Variables
Decision trees can handle missing values by creating branches that represent missing values as a separate category. Decision trees can also handle categorical variables by creating branches for each category of the variable.
Multiple Branches
Another important characteristic of decision tree analysis is that it can handle multiple outputs by creating multiple branches for each possible outcome which makes it easy for anyone to handle multiple problems at once. In simple words just like a Smartphone can perform multiple functions at once and solve a variety of problems like there is no need for wearing a watch, or keeping a torch or using the calculator on the Smartphone in the same way decision tree analysis can handle multiple problems and give multiple outputs.
Other Miscellaneous Features
Apart from the above features decision tree analysis has many other features such as the decision tree can handle imbalanced datasets, it can also handle high-dimensional data, it can handle non-linear interactions, can capture non-linear relationships between the variables, it can handle both categorical and numerical variables and so on.
As one can see from the above that decision tree analysis is a powerful and widely used tool in the field of data analysis and has many unique characteristics and that is the reason why any company thinking of using this method should carefully read the above points and then only should use it so as to take maximum advantage from this powerful tool as decision tree analysis can be used in various fields such as finance, healthcare, marketing, logistics, production and many more.