In machine learning, regression refers to supervised learning tasks where the output variable is continuous (numeric) rather than categorical. A regression model learns the relationship between input features and a continuous target, enabling it to predict a numeric value for unseen inputs​.For example, predicting house prices, stock prices, or temperature are regression problems. The model is trained on labeled examples (feature vectors with corresponding numeric outcomes) and adjusts its parameters to minimize the error between predictions and true values. Linear regression, polynomial regression, and regression trees are examples of regression algorithms. Essentially, regression analysis fits a function to data that can output a real-valued result, aiming to capture the underlying trends or patterns in the data​.