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Repeating model building for multiple labels
I have a dataset containing about 20 attributes and 6 numerical label variables I want to predict. I would like to use the same type of modeling process (NearestNeighbor with attribute weights determined by EvolutionaryWeighting, all inside a WrapperXValidation) to predict each label, allowing the attribute weights to be optimized separately for each label.
Ideally, I could iterate through each label to predict, using the same operator structure, rather than writing out 6 slightly different operator chains. Something like this pseudo-code:
For (predictvar in list_of_predict_vars)
Set label = predictvar
Do XVal - EvoWeights - NearestNeighbor model fit
Save model and performance results for this predictvar
Go to next predictvar
Generate predictions on original data using all 6 models
I suspect that using macros could get me close to doing this, and there seems to be some related approaches mentioned at http://rapid-i.com/rapidforum/index.php/topic,32.msg47.html and http://rapid-i.com/rapidforum/index.php/topic,35.msg64.html ; But I haven't quite figured out how to iterate through a user-defined list of values, and to change the label variable of a dataset using that list.