C4.5 algorithm

C4.5 is a machine learning algorithm used to create decision trees for classification tasks. It analyzes data with multiple features to find the most informative questions that split the data into groups with similar outcomes. The process involves selecting attributes that best separate different categories, based on a measure called information gain. The algorithm then builds a tree where each node represents a question, guiding the data down different paths until a classification decision is reached at the leaves. C4.5 is commonly used in areas like diagnosis, credit scoring, and data analysis to automate decision-making processes.