Support for Machine Learning Studio (classic) will end on 31 August 2024. We recommend you transition to Azure Machine Learning by that date.
Beginning 1 December 2021, you will not be able to create new Machine Learning Studio (classic) resources. Through 31 August 2024, you can continue to use the existing Machine Learning Studio (classic) resources.
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This article describes the modules in Machine Learning Studio (classic) that support creation of regression models.
Applies to: Machine Learning Studio (classic) only
Similar drag-and-drop modules are available in Azure Machine Learning designer.
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 (classic) 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:
Machine learning algorithm cheat sheet for Machine Learning
Provides a graphical decision chart to guide you through the selection process.
How to choose Machine Learning algorithms for clustering, classification, or regression
Explains in greater detail the different types of machine learning algorithms, and how they're used.
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
- Bayesian Linear Regression: Creates a Bayesian linear regression model.
- Boosted Decision Tree Regression: Creates a regression model by using the Boosted Decision Tree algorithm.
- Decision Forest Regression: Creates a regression model by using the decision forest algorithm.
- Fast Forest Quantile Regression: Creates a quantile regression model.
- Linear Regression: Creates a linear regression model.
- Neural Network Regression: Creates a regression model by using a neural network algorithm.
- Ordinal Regression: Creates an ordinal regression model.
- Poisson Regression: Creates a regression model that assumes data has a Poisson distribution.
For examples of regression in action, see the Azure AI Gallery.