Choose a model based on feedback
This "multi ensemble" (see details) evaluates each models feedback (e.g. error), and the best one is chosen to be used for the determination of new optimal candidate points. In order to allow for randomization, the epsilon parameter can be set to values between 0 and 1, where 1 means completely random (feedback is ignored) and 0 means completely deterministic (choice only dependent on feedback, not random).
Relevant configuration parameters of this ensemble are:
The function to calculate feedback (e.g. the error) of the models, default is
The function average model predictions, default is
The number of designs created for validation, which should be zero, i.e.
spotConfig$seq.ensemble.cut.num <- 0
The epsilon parameter, default is
spotConfig$seq.ensemble.epsilon <- 0
new design points which should be predicted
global list of all options, needed to provide data for calling functions
if an existing model ensemble fit is supplied, the models will not be build based on data, but only evaluated with the existing fits (on the design data). To build the model, this parameter has to be NULL. If it is not NULL the parameters mergedB and rawB will not be used at all in the function.
This is a "multi ensemble", meaning that in every sequential step all models in the ensemble are trained and evaluated.
The target is to actively combine all models responses, to get the best estimate on which candidate points are optimal.
The models used are specified in the
spotConfig list, for instance:
spotConfig$seq.ensemble.predictors = c(spotPredictRandomForest, spotPredictEarth, spotPredictForrester, spotPredictDace)
To specify the settings of each individual model, set:
seq.ensemble.settings = list(list(setting=1),list(setting=2),list(setting=3),list(setting=4))
Any parameters set in each of the corresponding lists (here: 4 individual lists) will overwrite settings in the main
when the concerned model function is called.
returns the list
- M. Friese, M. Zaefferer, T. Bartz-Beielstein, O. Flasch, P. Koch, W. Konen, and B. Naujoks. Ensemble based optimization and tuning algorithms. In F. Hoffmann and E. Huellermeier, editors, Proceedings 21. Workshop Computational Intelligence, p. 119-134. Universitaetsverlag Karlsruhe, 2011.
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