REGRESSION

Regression in data science is a predictive modeling technique that uses the relationship between one or more independent variables and a dependent variable. Regression analysis is a method used in both statistics and machine learning, modeling the mathematical relationship between variables. As a result, the model attempts to predict the future values that the dependent variable may take based on new data inputs. Therefore, the question "What is regression?" can be answered as a statistical and machine learning method that enables predictions based on the relationship between the independent variables believed to influence the outcome and the dependent variable being predicted.

Types of Regression

Regression is commonly categorized into several types, including simple linear regression, multiple linear regression, logistic regression, polynomial regression, Ridge regression, and Lasso regression. You can explore these regression types in the list below.

• Linear Regression: Simple linear regression is used to model the relationship between a single independent variable and a dependent variable. For example, a simple linear regression model can be used to predict a student's exam score based on the amount of time spent studying.
• Multiple Linear Regression: This is a predictive method that models the relationship between multiple independent variables and a single dependent variable. In the real estate industry, for example, house prices can be predicted using independent variables such as square footage, number of rooms, building age, and location.
• Logistic Regression: Unlike other types of regression, logistic regression predicts the probability of belonging to a specific class rather than a continuous value, and performs classification based on that probability. For example, when logistic regression is used to predict whether a server will fail, the outcome can be expressed as categories such as "Yes" or "No."
• Polynomial Regression: Polynomial regression is used when the relationship between variables is not linear. For example, polynomial regression can be used to model the nonlinear relationship between server load and response time. Although response time generally increases as the number of concurrent users grows, this increase is not always linear. Performance degradation may become more pronounced, particularly as the system approaches its capacity limits.
• Ridge and Lasso Regression: Ridge and Lasso regression, which are considered regularization techniques, help prevent overfitting by reducing model complexity. While Ridge regression shrinks the coefficients of all variables, Lasso regression can reduce some coefficients to zero, effectively removing unnecessary variables from the model. For example, when predicting the price of a house using variables such as square footage, number of rooms, building age, location, door color, and the number of electrical outlets in the bathroom, the influence of variables that do not make a meaningful contribution to the model can be reduced or eliminated entirely.

For more information on this topic, you can read our blog post titled What Is Regression Analysis? Its Role in Data-Driven Decision-Making.