Machine Learning - Initialize Model - Regression

This article describes the modules in Azure Machine Learning Studio that support creation of regression models.

Note

Applies to: Machine Learning Studio

This content pertains only to Studio. Similar drag and drop modules have been added to the visual interface in Machine Learning service. Learn more in this article comparing the two versions.

More about regression

Regression is a methodology used widely in fields ranging from engineering to education. For example, you might use regression to predict the value of a house based on regional data, or to create projections about future enrollment.

Regression tasks are supported in many tools: for example, Excel provides "What If" analysis, forecasting over time, and the Analysis ToolPak for traditional regression.

The modules for regression in Machine Learning Studio each incorporate a different method, or algorithm, for regression. In general, a regression algorithm tries to learn the value of a function for a particular instance of data. You might predict someone's height by using a height function, or predict the probability of hospital admission based on medical test values.

Regression algorithms can incorporate input from multiple features, by determining the contribution of each feature of the data to the regression function.

How to create a regression model

First, select the regression algorithm that meets your needs and suits your data. For help, see these topics:

Add training data. Be sure to consult the module reference for each algorithm in advance, to determine if the training data has any special requirements, other than a numeric outcome.

To train the model, run the experiment. After the regression algorithm has learned from the labeled data, you can use the function it learned to make predictions on new data.

List of modules

Examples

For examples of regression in action, see the Azure AI Gallery.

See also