Fit a One-Stage Model

A one-stage model fits a model to all the data in one process. If your data inputs do not have a hierarchical structure, and all model inputs are global at the same level, then fit a one-stage model.

If your data has local and global inputs, where some variables are fixed while varying others, then choose a two-stage or point-by-point model instead. See Fit a Two-Stage Model or Fit a Point-by-Point Model. After you import data, follow these steps to fit a one-stage model:

1. In the Model Browser home page, click Fit Models.

2. In the Fit Models dialog box, select a data set in the project from the Data set list.

   If you have no data loaded, you can click Import from file in the Data pane. Use the file browser to select a file to import.

   Optionally, you can select validation data as a sample of the fitting data or a separate data set.

3. Click the One-Stage test plan icon in the Template pane.

4. In the Inputs and Responses pane, select data channels to use for the responses you want to model, and click the button to add to the responses.

   To create a boundary model, leave the Fit boundary model check box selected. A boundary model describing the limits of the operating envelope can be useful when you are creating and evaluating designs, optimization results, and global models.

5. Select data channels to use for the model inputs, and click the button to add to the responses.

6. Click OK to fit the default model types to your selected data. The toolbox calculates the fit and adds a new model node to the Model Tree. The default response model type is a Gaussian process model (GPM) which can usually produce a good fit first time.

   If you are using a template that you created, to override the default models, clear the Use default models for large data option.

   You can also select Convex hull or Pairwise convex hull from the Boundary model list to override the default boundary model setting.

   Default Model Types	Large Data Settings for >2000 Points
   Response model: Gaussian process model (GPM)	Uses the large data behavior for Gaussian process models from Statistics and Machine Learning Toolbox™.
   Boundary model: Convex hull fit to the inputs	Switches to pairwise convex hull. Switch when ≥ 8 inputs even when <2000 points.

After you fit your model, assess the model fit. See Assess One-Stage Models and Guidelines for Selecting the Best Model Fit. You can also create more models for comparison to search for the best fit. See Create Alternative Models to Compare.

Tip

To view an example project with engine data and finished models, see Spark Ignition (SI) Calibration Workflow.