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Putting constraint on neural network's output in Rapidminer

amirh_hassanpouamirh_hassanpou Member Posts: 1 Learner II
edited November 2018 in Help

I am developing a simple neural network model in Rapidminer to predict the number of cars passing on a highway every hour. As it is obvious, in the early morning (from 2:00 am to 6:00 am) few cars are on the highway and sometime my model predicts the number of cars to be negative (like -2 or -3), which is understandable statistically but is not cool when you want to report it somewhere.

I am looking for a way to put constraint on the model so that it would only predict positive numbers. How can I do that?

Thanks

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Answers

  • BalazsBaranyBalazsBarany Administrator, Moderator, Employee-RapidMiner, RapidMiner Certified Analyst, RapidMiner Certified Expert Posts: 955 Unicorn

    Hi,

     

    there's usually no way to put numerical constraints directly into models. You want them to be unbiased by expectations and "give their best".

     

    But you can always put a Generate Attributes behind the prediction step and change impossible values to 0.

    E. g. prediction = if([prediction] < 0, 0, [prediction])

     

    Regards,

    Balázs

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