R-squared vs number of basis functions plot

Note

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

The R-squared vs number of basis functions plot displays R² values on the y-axis and the number of basis functions on the x-axis. The R² value indicates whether the model is a good fit. Use the test results to assess the performance of the model to predict new observations. Compare the training results and the test results to see whether the model for the training data set has an overfitting problem.

The largest x-value for a point shows that this analysis evaluates 20 basis functions. The optimal number of basis functions is 13. The R² value for the test data when the number of basis functions is 13 is approximately 87.61%.

When the absolute deviation loss function determines the optimal model, then the results include the MAD vs number of basis functions plot instead.

Interpretation

Higher values of R² indicate a better model. The reference line indicates the optimal R² value for the test data and the number of basis functions in the model. If the test curve indicates an insufficient model, consider whether to retry the analysis with alternative settings, such as a search of more basis functions.