What is a decision tree in machine learning?

A decision tree is a type of supervised learning algorithm that is used for classification and regression tasks. It works by recursively splitting the data into subsets based on the values of the input features, with the goal of maximizing the information gain or minimizing the impurity at each split. Decision trees are easy to interpret and visualize, and can handle both categorical and numerical data. However, they can be prone to overfitting and are sensitive to small changes in the data.