Creating an Approximation Using the RBF Model

You can create an approximation using the Radial Base Function (RBF) model and tune the approximation options. You can use the Predict page to compare the quality of your approximations.

See Also
A Quality View of Your Approximations
Approximations
The Radial Base Function Model
Understanding Your Approximation
  1. From the Approximations table on the left side of the Predict page, select .

    Note: If no approximations have been created, you can also click Create New Approximation.

    The Create New Approximation dialog box appears.
  2. From the Algorithm tab, enter a name for the approximation.
  3. From the Type menu, select Radial Base Function.

    Note: If desired, you can create approximations using both models and compare the approximations side by side or in an overlay plot to determine which model produced the best numerical solution.

  4. Enter the maximum number of data points used during the calculation of the approximation.

    By default, Results Analytics calculates the approximation using a maximum number of 2000 data points. The accuracy of the approximation increases with more data points; however, the length of time needed for Results Analytics to fit the approximation also increases. You may have to experiment with the number of points and the approximation options to find a balance between accuracy and speed.

    If your data set contains more data points than the maximum specified, Results Analytics searches through the entire data set and selects points randomly to include in the approximation.

  5. Enter a value for the Smoothing Filter (0 ≤ value ≤ 0.1).

    Results Analytics uses the smoothing filter to relax the requirement that the Radial Base Function Model approximation passes through every single data point. The smoothing filter's primary purpose is to smooth out noisy data. By not going through every point, Results Analytics can effectively smooth noisy functions and provide an approximation that may be easier to optimize. The value specified by this option averages the output values of points that are clustered in the normalized filter domain.

  6. From the Datasets tab, select datasets to exclude from approximation generation.
  7. From the Inputs tab, select input parameters to exclude from the generation of a specific approximation.

    These inputs will not be used in training and will not be displayed in the prediction profiler.

  8. From the Validation tab, set the validation type and validation hyperparameters such as Number of Folds for K-Folds Cross Validation.
  9. Click Create to create the approximation.
    The approximation is created with the name you specified and appears in the list of approximations.