Overview of Discover Best Model (Continuous Response)


This command is available with the Predictive Analytics Module. Click here for more information about how to activate the module.

Usually, the easiest way to determine which type of model makes the best predictions for a specific data set is to build all of the models and compare the performance. Use Discover Best Model (Continuous Response) to compare the performance of 5 common types of models for a continuous response with many categorical and continuous predictor variables. For example, real estate appraisers want to predict the sales prices of properties with many predictor variables such as the square footage, the number of available units, the age of the building, and the distance from the city center. The appraisers compare the performance of the different types of models to decide how to get the most accurate predictions.

Among the 5 model types are 2 more general types of models: multiple regression models and tree-based models. Fit Regression Model makes multiple regression models. CART® Regression, TreeNet® Regression, and Random Forests® Regression make tree-based models. MARS® Regression is a special type that combines features of both multiple regression models and tree-based models.

For descriptions of the different model types, go to Types of predictive analytics models in Minitab Statistical Software.

Where to find this analysis

To find the best model to predict a continuous response, choose Predictive Analytics Module > Automated Machine Learning > Discover Best Model (Continuous Response).

When to use an alternate analysis

If you have a binary response variable, use Discover Best Model (Binary Response).