Decision trees are a popular method used in machine learning for making decisions based on data. They work by splitting the data into branches based on certain criteria, leading to a final decision or classification at the leaves of the tree. This visual representation makes it easy to understand how decisions are made, as each branch represents a choice based on the input features. One of the key advantages of decision trees is their interpretability. Unlike some complex models, decision trees can be easily visualized and understood by non-experts. They can handle both numerical and categorical data, making them versatile for various applications, from finance to healthcare.