Decision trees: Why use them?

The non-parametric supervised learning approach used for classification and regression applications is the decision tree. It is organized hierarchically and has a root node, branches, internal nodes, and leaf nodes.

By using a greedy search to find the ideal split points inside a tree, decision tree learning uses a divide and conquer technique. When most or all of the records have been classified under distinct class labels, this splitting procedure is then repeated in a top-down, recursive fashion. The intricacy of the decision tree plays a significant role in determining whether or not all data points are categorized as homogenous sets.

Decision trees: Why use them?

The most important thing to keep in mind while developing a machine learning model is to select the optimal method for the dataset and task at hand. The two rationales for employing the decision tree are as follows:

Decision trees are typically designed to resemble how people think when making decisions, making them simple to comprehend.

Because the decision tree displays a tree-like structure, the rationale behind it is simple to comprehend.

Decision tree applications

Evaluation of potential business growth chances based on past data is one of the applications of decision trees.

Using demographic information to identify potential customers is another way that decision trees are used. They can aid in streamlining a marketing budget and helping to decide on the target market that the company is concentrating on with knowledge.

Decision trees can also be utilized in operations research for strategic management and logistical planning. They can assist in choosing the best tactics to help a business accomplish its stated objectives. In the fields of engineering, education, law, business, healthcare, and finance, decision trees can be used.

Conclusion

Here, we discussed about decision tree , why we used decision tree and application of decision tree . You can visit advantages of decision tree here.