However, the term "regression" can be interpreted loosely, and some types of regression provided in other tools are not supported in Studio (classic). Machine Learning Studio (classic) supports a variety of regression models, in addition to linear regression. Linear regression also tends to work well on high-dimensional, sparse data sets lacking complexity. Linear regression is still a good choice when you want a very simple model for a basic predictive task. In the most basic sense, regression refers to prediction of a numeric target. Linear regression is a common statistical method, which has been adopted in machine learning and enhanced with many new methods for fitting the line and measuring error. Alternatively, the untrained model can be passed to Cross-Validate Model for cross-validation against a labeled data set. The trained model can then be used to make predictions.
You use this module to define a linear regression method, and then train a model using a labeled dataset. Linear regression attempts to establish a linear relationship between one or more independent variables and a numeric outcome, or dependent variable.
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This article describes how to use the Linear Regression module in Machine Learning Studio (classic), to create a linear regression model for use in an experiment. Similar drag-and-drop modules are available in Azure Machine Learning designer. Applies to: Machine Learning Studio (classic) only
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