What is decision tree?

A decision tree is a flowchart-like structure in which each internal node represents a feature (or attribute), each branch represents a decision rule, and each leaf node represents an outcome. The topmost node in a decision tree is known as the root node. It learns to partition on the basis of the attribute value. It partitions the tree recursively in a manner called recursive partitioning.

Decision Trees are one of the easiest and popular classification algorithms to understand and interpret. It can be utilized for both classification and regression kind of problems.

Here's a simple example of a decision tree:

In this example, the decision tree makes predictions based on the size and price of a house. The internal nodes are decisions based on these features, the branches are the outcomes of these decisions, and the leaf nodes are the final predictions.

In a machine learning context, decision trees learn from data to approximate a sine curve with a set of if-then-else decision rules. The deeper the tree, the more complex the decision rules and the fitter the model.