Glossary

Linear Regression

What is Linear Regression?

Linear regression models a relationship between input variables and an output variable and can be used to predict the value of a response to a new set of inputs. A simple linear regression is a line fitted to existing pairs of one independent variable and a dependent variable, with a parameter designating the slope of the line and a bias the y-intercept. Fitting the line or curve to the data is accomplished by minimizing the error between the observed values and the formulated values using some form of loss function like least squares, ridge regression, or lasso.

 

Why is Linear Regression Important?

Linear regression is a fundamental algorithm used in supervised learning cases because of its simple and well-understood application to a variety of problems like predicting trends in science, healthcare, finance, economics, and consumer behavior. Linear regression supports several estimation techniques, loss functions, and convergence methods to fit different optimization and time/cost goals.

 

How C3.ai Enables Organizations to Use Linear Regression

C3.ai makes it easy to apply different optimization techniques like linear regression to address domain-specific applications of AI to deliver business value today. The C3 AI® Suite is a complete, end-to-end platform for designing, developing, deploying, and operating enterprise AI applications at industrial scale. Data scientists can use the C3 AI Suite’s ML/AI services to get a full view into the source data, explore and develop model features, and test and evaluate different ML models and their predictive performance.

Linear regression is one of many statistical and modeling techniques in production on the C3 AI Suite, which includes basic statistics, supervised and unsupervised learning, reinforcement learning, optimization, collaborative filtering, dimensionality reduction, and deep learning.