Regression Models in ML: Examples & Use Cases

In machine learning (ML), regression models provide powerful predictive capabilities. By investigating the relationships between independent and dependent variables, regression techniques such as linear regression can accurately predict continuous values or outcomes. In this article, we’ll look at what regression analysis is, highlighting seven popular regression models with examples of the real-world business problems they solve. 

Regression is a supervised learning technique that models the relationship between input features and a continuous target variable, using statistical methods to predict the target variable based on new input data. Regression models sift through large numbers of variables, identifying those with the greatest impact outcomes. Regression is foundational to machine learning, especially for predictive use cases. By fitting a regression model to data, organizations can replace educated guesses and hunches with data-driven insights into the factors most likely to drive future outcomes and behaviors. 

To illustrate, an organization could use linear regression, the simplest type of machine learning regression model, to forecast future sales based on advertising spend. In this example, the independent variable is advertising spend, the factor that can be adjusted for and controlled. The dependent variable would be sales, the outcome we’re attempting to predict based on changes in ad spend. The linear regression model would find the best-fitting line through a set of data points to predict the relationship between sales and ad spend, providing the insights needed to achieve the highest possible sales or revenue for the least amount of advertising expenditure.

In machine learning, there are many types of regression models, each with strengths for specific data scenarios and prediction needs. These examples highlight the diversity and versatility of regression techniques across diverse domains, including how they’re applied in real-world contexts.

Linear regression is a statistical method that uses known-value data to predict the value of unknown data. The relationship between a dependent and independent variable or variables is modeled by fitting a linear equation to observed data. Linear regression methods excel at detecting patterns in historical data, providing marketing and sales teams with a detailed understanding of how customer behavior, service usage, pricing and demographic data impact churn rates. Multiple linear regression can help businesses predict customer churn by identifying and quantifying the primary drivers prompting a customer to leave.

Polynomial regression is an advanced form of linear regression used to capture complex patterns in data. It models the relationship between the dependent and independent variables as an nth degree polynomial. By fitting a nonlinear equation to the data, it can capture nonlinear relationships, making it useful when working with complex data sets. This type of regression model is commonly used in financial services applications. With the ability to capture nonlinear interactions between variables like age, driving history and vehicle type, polynomial regression allows insurers to better assess risk factors and predict outcomes, resulting in more informed underwriting decisions.

Ridge regression is a statistical regularization method used to correct overfitting on machine learning model training data. Ridge regression is a good choice for analyzing multicollinearity, the occurrence of high intercorrelations among two or more independent variables within a multiple regression model. This prevents overfitting by adding a penalty to the regression coefficients. In healthcare settings, ridge regression is used to identify the relationship between a large number of genetic, lifestyle and environmental factors and the risk of developing specific diseases. This type of regression can play an important role in building more powerful, reliable models for predicting individual disease risk based on many complex, interrelated factors.

Least Absolute Shrinkage and Selection Operator (Lasso) regression,  is a form of linear regression that uses shrinkage, with data values being shrunk toward a central point, such as the mean. A primary use case for lasso regression is automating feature selection. Lasso regression automatically selects useful features, eliminating unneeded or redundant features.

Elastic net regression merges the penalties of lasso and ridge regression together, resulting in a machine learning regression model that can balance between variable selection and handling multicollinearity in predictive models. In the context of sports analytics, elastic net regression’s ability to handle a broad range of correlated variables — such as player statistics, physical metrics and game conditions — makes it useful for analyzing player performance and predicting game outcomes.

Logistic regression is a statistical method used for predicting binary outcomes using one or more predictor variables. Using a data set of independent variables, this model estimates the probability of an event occurring. Logistic regression can play an important role in manufacturing settings with predictive maintenance, estimating the likelihood of equipment failure based on factors including usage patterns, operating conditions and data from past failures. This predictive capability helps organizations perform equipment maintenance proactively, boosting operational efficiency while reducing maintenance costs.

Gradient boosting is an ensemble machine learning model that can be used to solve complex regression problems. Through the successive addition of weaker predictive models, gradient boosting seeks to minimize the overall prediction error by combining the strengths of many models, most often decision trees. The highly accurate final prediction represents the average of the weak learnings. Gradient boosting is especially useful in answering sales-related business questions because it can handle complex patterns and interactions between variables. For example, it can analyze historical sales data, seasonal trends and other factors such as economic indicators, weather patterns and shifts in consumer demand to generate accurate and reliable sales forecasts.