This document provides an introduction to decision trees, which are a type of predictive model that uses a tree-like structure to determine the class of records. Decision trees work by recursively splitting a dataset into purer subsets based on attribute values, resulting in a flowchart-like structure. They have an intuitive appeal as rules can be represented visually or as "if-then" statements. The document discusses key aspects of decision trees such as how they are constructed, evaluated, pruned to prevent overfitting, and their advantages and limitations for classification tasks.