BlackBox is one of the core components of EYE. It aggressively
hunts for the model that best explains the data
.
BlackBox searches over a wide variety of models, and incremental reports on its findings scroll down the screen as it runs. As well as considering different kinds of function approximator, such as nearest neighbor and kernel regression, it also searches for the best attribute subsets (determining whether any input variables can be ignored, and what relative weights should be given to the remaining inputs). Each of the models typically has several parameters that need to be tuned, such as distance metric parameters and smoothing parameters. Blackbox autonomously searches for the optimal values of these parameters. It uses multiple levels of cross-validation to police itself against overfitting.
Because searching over all models may take a considerable time , the user can set the Blackbox seconds parameter to impose an upper limit on the execution time. When the time limit is reached, BlackBox stops running and produces a summary of its findings.
If you have run BlackBox earlier on--and if you are still using the same datafile and parameter settings--then a new call to BlackBox will resume from where it last stopped, rather than duplicating earlier work. Note that BlackBox will also refer to any results discovered during the EYE function Search (see section 6.7).
BlackBox uses the following parameters: Classification/Regression, Blackbox seconds, Blackbox test, No. crossval, Max. No. Attributes, Testfile.
Blackbox sets the GMString parameter to the best function approximator that it has found for modeling the data.