What is Regression in Supervised Learning?
Regression is a fundamental concept in supervised learning, a subset of machine learning, which falls under the broader category of artificial intelligence. In supervised learning, algorithms are trained using labeled datasets, where the input features and corresponding output labels are known.
Regression specifically focuses on predicting continuous output values. Unlike classification tasks, which categorize data into discrete classes, regression aims to find a function that best approximates the relationship between the input variables and the continuous output. This is often visualized with a line of best fit in two-dimensional space, where one axis represents the input features and the other represents the predicted values.
Common techniques used in regression include linear regression, polynomial regression, and more advanced methods such as support vector regression and regression trees. Each approach utilizes different algorithms and mathematical principles to minimize prediction error and enhance accuracy.
The applications of regression are vast and varied, ranging from finance—predicting stock prices—to healthcare—estimating patient outcomes based on historical data. By understanding regression, practitioners can make informed decisions based on data-driven insights, fundamentally enhancing the predictive capabilities of their systems.