What is a decision tree?

A decision tree is a model that makes decisions by forming a structure that resembles a tree, where each node represents a decision point based on specific data features. The process begins with a root node, which splits into branches based on the answers to a series of questions regarding the features of the data. Each subsequent node continues to branch until a leaf node is reached, representing the final decision or classification outcome. This approach allows for intuitive visualization of the decision-making process, making it easier to understand how decisions are made in terms of the underlying data characteristics.

The nature of decision trees facilitates analysis of the data, as they explicitly show the criteria that led to specific outcomes. This is especially useful in contexts such as classification problems, where the goal is to assign a label to an entity based on its features. The clarity and interpretability of decision trees make them a popular choice in various applications, including business, medicine, and machine learning.

Other choices may refer to different concepts within data science and machine learning. For instance, sorting based on statistical measures could relate to data preprocessing but does not describe the decision-making process inherent in decision trees. A type of neural network architecture would be more accurately defined by structures such as convolutional neural networks or recurrent neural networks, which