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Cross- Validation of R, Python Models

bhupendra_patilbhupendra_patil Administrator, Employee, Member Posts: 168 RM Data Scientist
edited November 2018 in Knowledge Base


RapidMiner provides ability to work with many learners in a code-free manner. However if needed one can also extend the capabilities of RapidMiner  using R & Python scripts. Even when using R or Python many of core RapidMiner's operators can still be utilized to get the best of the both worlds.


The following example showcases how to use Cross-Validation operator and performance operators along with R Script. Please use the attached example process file to try it yourself


Step 1 ) Prepare your data and pass it to "tra" port on the cross validation operator as seen below



As you may be aware inside the X-validation operator has two sub process. We will write our Model Training script in the training part using the execute R operator. On the testing part we will write the "Apply model" R script and use a performance operator from RapidMiner  to capture performance.



inside cross validation.png



Please note that the training script returns a model, that is passed along to the "mod" part. It is then passed to the second testing side to the first "inp" port. When using the R Script operator, the parameters are passed by order in which they are connected.

So you will notice that on training side, since we are passing model first, and then 'tes" which is your test data, the script needs to receive them in the same order. Hence the first parameter in rm_main on testing side is model and second is the data.


You are then using the regular RapidMiner  performance operator to capture the performance.



The training and test scripts looks like below


training script.png



testing scripts.png

Similar techniques can be applied to combine scripting operators with other operators.





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    jesus_tobonjesus_tobon Member Posts: 1 Contributor I

     Thanks for your help, Could You share the process file?

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