Decision Tree : Why are Decision Trees used?

A decision tree can be used in decision analysis to artistically and explicitly portray decisions and decision making. It employs a decision-tree-like model, as the name implies. Though a common tool in data mining for developing a strategy to achieve a specific goal, it is also widely used in machine learning.

It is a versatile tool with a wide range of applications. Decision trees can be used to solve classification and regression problems. The name implies that it employs a flowchart-like tree structure to display the predictions resulting from a series of feature-based splits. It begins with a root node and ends with a leaf decision.

Why are Decision Trees used?

Because there are numerous algorithms in machine learning, the main thing to remember when developing a machine learning model is to select the best algorithm for the provided dataset and problem. The two reasons for using the Decision tree are as follows:

Decision trees are designed to mimic human decision-making abilities, making them simple to understand.

Because the decision tree has a tree-like structure, the logic behind it is easily understood.

How does the Decision Tree algorithm Work?

In a decision tree, the algorithm begins at the root node and works its way up to predict the class of a given dataset. This algorithm checks the values of the root attribute with the values of the record (real dataset) attribute and then continues to follow the branch and jumps to the next node based on the comparison.

Conclusion

In this blog, we saw the decision tree, why we used it, and how does the Decision Tree algorithm Work .

You can also read advantages of decision tree here.