CatBoost

CatBoost is a machine learning algorithm designed to help computers make accurate predictions using structured data. It builds a series of decision trees—like flowcharts—that split data based on different features to find patterns. What sets CatBoost apart is its ability to handle categorical variables (like colors or categories) directly without extra processing and to prevent overfitting, resulting in reliable results. It's widely used in tasks such as ranking, classification, and regression, enabling organizations to analyze data efficiently and improve decision-making in areas like finance, marketing, and search engines.