A decision tree is a tool used for decision-making. It makes use of a tree-type model or graph of decisions and incorporates their possible consequences. Utility, resource costs, event outcomes and the like are also included. It allows for the algorithm to only contain control statements that are conditional. The decision tree is similar to any other flowchart structure as it contains an internal node that represents each test of the attribute. It could be whether the coin flipped turns out to be tails or heads. The outcome of the test is represented by the class label. The paths to the leaf from the root help represent the classification rules.  

Algorithms that are tree based are considered as some of the best. They are used as a super learning technique most of the time. Predicted models are empowered by tree based methods with easy interpretation, stability, and high accuracy. Tree-based techniques help map out non-linear relationships effectively unlike other linear models. A great thing about them is adaptability to solving just about every type of problem from regression to classification. Classification and Regression Trees or CART for short is what decision tree algorithms are called. Techniques such as gradient boosting, random forest, and decision trees are widely used when dealing with data science issues. Some of the common terms that are used in decision trees are mentioned as follows.

• Splitting: A process that divides the node into more sub-nodes.
• Root Node: It allows the sample or even the entire population to be represented. Then, it is further divided into more homogeneous sets. 
• Pruning: When sub-nodes are removed from a decision node, the process is known as pruning. It is the opposite of splitting.
• Terminal/ Leaf Node: Nodes which are unable to split into further sub-nodes are referred to as terminal or lead node.
• Decision Node: It is the process where the sub-node splits. The further-nodes are known as the decision nodes.
• Child and Parent Node: When a node is divided into sub-nodes, it is referred to as the child node or the above sub-node would be called the parent node.

Decision Tree Application

The construction of decision trees offers many applications, from group users and reinforcement learning to optimization. They can easily fit into a programmable structure for the best results. When it comes to the categorization of problems, decision trees come in handy. They are extremely effective where features and attributes can be checked systematically for the determination of a final category. For instance, the decision tree method can be used for determining an animal’s species. 

In Machine Learning and Data Mining, decision trees are commonly used as a classification algorithm. Some of the applications include the following.

• Prediction of the likelihood for an application borrower to default by using the predictive models that use historical data.
• The determination of buyers who are likely to purchase a product or service with the help of demographic data. It fully utilizes the limited advertising budget. 
• Historical sales data is used for the evaluation of brand growth.
• If there is a need for emergency treatment, then it is possible with a predictive model that is based on location, gender, blood pressure, age, severity of pain, and other factors. 

When it comes to research, decision trees are widely used, especially in decision analysis as they help identify the strategy that offers the best chances of accomplishing a goal. Tree diagrams are used in various disciplines and industries due to their simplicity, such as in the education industry, pharmaceutical industry, the healthcare industry, the financial industry, and the like. 

Benefits of Decision Trees

There are various benefits of decision trees. Some of these are mentioned below. 

• When it comes to data exploration, decision trees are extremely useful. They are able to identify the most important variables quickly when comparing between other variables. Moreover, new variables can also be created with decision trees to better predict the target variable.
• The best thing about using decision trees as a predictive method is that their output can be easily understood by just about anyone that does not even have an analytical background. There is no need to have any statistical knowledge. 
• Little to no effort is required by decision trees in the form of less data preparation.
• There is also less of a need to clean the data unlike other modeling methods.

To conclude, to implement decision trees and data analytics for your business, get in touch with NextBee. With over 10 years of experience in providing services related to data science and other methods, NextBee has the experience needed for offering you the best solution.