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Optimize Decision Tree and Optimize SVM

BalazsBaranyBalazsBarany Administrator, Moderator, Employee, RapidMiner Certified Analyst, RapidMiner Certified Expert Posts: 473   Unicorn
edited November 2018 in Knowledge Base

Two building blocks for doing grid parameter optimization on an example set. 


Decision tree: confidence, minimal gain, minimal size for split, criterion


SVM: kernel type, C, epsilon


Input1: Example set (should be ready for modeling, e. g. with label, only numeric attributes for SVM etc.)


Outputs: Performance, Parameters, Log


  • s_webermiks_webermik Member Posts: 1 Contributor I


    thanks, the decision tree block worked for me, but how can I see the decision tree with those optimized parameters?


    With this I just get the performance, log, etc.; which became better but I cannot work with this to predict the important attributes.


    Thanks & regards,


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