Optimisation Grid - Model Parameters for Training and Testing
Hi,
I am new to Rapidminer and am confused about cross validation, optimisation grid, train and testing.
I have 2 separate datasets i.e. 1 for training and 1 for testing. I want to build a SVM model with cross validation and to use optimisation grid to get the best hyperparameters. My questions are,
1. Do I nest the optimisation grid inside the cross validation operator or do I nest the cross validation operator inside the optimisation grid?
2. Once the optimisation is completed, do I manually get the parameters eg C and gamma values for the SVM model and build separately a SVM model and use an Apply Model & Performance operator with the test data? Or there is a better way to do this? A picture of the process flow is much appreciated as I am unable to visualise how it looks.
Thanks,
Lobbie
Best Answer
-
You put the cross-validation into your optimization grid operator, and then select the parameters you want to optimize. The optimization operator will then output the optimized model for you as well as a report of what the selected parameters were. If you pull the Optimize (Grid) operator into your process pane and then look at the help, there is a link to open a tutorial process (this is true of most operators, by the way), and it will show you a process with the most common configuration. That should get you started.
0
Answers
-
You put the cross-validation into your optimization grid operator, and then select the parameters you want to optimize. The optimization operator will then output the optimized model for you as well as a report of what the selected parameters were. If you pull the Optimize (Grid) operator into your process pane and then look at the help, there is a link to open a tutorial process (this is true of most operators, by the way), and it will show you a process with the most common configuration. That should get you started.
0 -
Brian, thank you for your help. Much appreciated.
0