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# Optimization Grid with Random Forest - Not Working.

RapidMiner Unicorns 🦄,

I trying to run a optimization grid with our my Random Forest model and I am getting an error. It's stating that gain_ratio criterion cannot be used for numeric labels (see pictures below). I checked all my parameters and I am not using gain_ratio in the optimization grid (see pictures below). So, specifically how you used a optimization grid with cross validation, and random forest predicting a real number in RapidMiner?

Can you send an basic working example of this workflow process with with good documented comments explaining each step.

I trying to run a optimization grid with our my Random Forest model and I am getting an error. It's stating that gain_ratio criterion cannot be used for numeric labels (see pictures below). I checked all my parameters and I am not using gain_ratio in the optimization grid (see pictures below). So, specifically how you used a optimization grid with cross validation, and random forest predicting a real number in RapidMiner?

Can you send an basic working example of this workflow process with with good documented comments explaining each step.

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## Answers

3,525RM Data ScientistDortmund, Germany

6Contributor II3,525RM Data ScientistDortmund, Germany

6Contributor IISpecifically what configuration/setup tasks are needed to make the grid optimization operator work and simply find the optimal parameters for Random Forest model? Do you have a sample workflow of how this can work?

6Contributor IISpecifically what configuration/setup tasks are needed to make the grid optimization operator work and simply find the optimal parameters for Random Forest model? Do you have a sample workflow of how this can work?

955UnicornJust select correct and applicable settings for the optimization. Leave the criterion alone (it has to be least_square for numerical prediction) and optimize parameters like the number of trees and the maximum depth.

Regards,

Balázs

6Contributor IIspecific, step by step, how-to instructionsto make this work. Thank you much.