Search allows the user to guide the search for the best model
for the data
. In contrast, the EYE function
BlackBox autonomously seeks the best model. The results discovered
during a user-guided Search will be remembered and referenced by
later calls to BlackBox. Likewise, Search will refer to
the results found during earlier runs of BlackBox, so as not to
duplicate earlier work.
If you select the Search function, EYE will bring up a dialog box to help you specify what you wish to search for. Options include searching for the best amount of smoothing, the best distance metric parameters, and the best attribute subsets. Once you have made your selection, incremental results will start scrolling down the screen as EYE performs the search, just as in BlackBox. When Search is finished, it displays a summary of its findings.
To prevent EYE from spending too long hunting for the very best model, the user can set the Blackbox seconds parameter to limit the execution time. When the time limit is reached, Search stops running and produces a summary of its findings.
Search polices itself against overfitting by using multiple levels of cross-validation. If Search discovers a better model than any found beforehand, it will set the GMString to an encapsulation of this model. (See section 5.11 for an explanation of GMStrings.)
Search uses the following parameters: Classification/Regression, Blackbox seconds, Blackbox test, No. crossval, Max. No. Attributes, Testfile.