Apply Model in RapidMiner

HeikoeWin786

Dear all,

I split a dataset 75%(Training) and 25%(Testing). I pre-processed the data on training data. Then, I split the training data again (75%-25%) and perform Naive Bayes Classification. I saved the model as a result of training data. 
However, if I want to test whether my model is applicable or not, I believe I need to run my model on the 25% which I kept as the test data initially. 
Means, for 25% of initial testing data, I will perform pre-processing same as training, then, I retrieve the pre-processed test data and apply the model which I saved from the training data. 
Could you please advise if this is correct or am I doing wrong here?

thanks.
Heikoe

Telcontar120

That would be correct, if you want to run split validation manually, which it sounds like you did.
You could also use the Split Validation operator, which does this automatically and delivers the performance on the test portion of the data.
But I would really recommend using Cross Validation instead, which will train and test on multiple subsets of your original data and supply the resulting validation performance automatically.

Telcontar120

That would be correct, if you want to run split validation manually, which it sounds like you did.
You could also use the Split Validation operator, which does this automatically and delivers the performance on the test portion of the data.
But I would really recommend using Cross Validation instead, which will train and test on multiple subsets of your original data and supply the resulting validation performance automatically.

sara20

HeikoeWin786

Hello

Most of the people have this question when they start working with data with RM or other software.
I agree with @Telcontar120, also I recommend you to read this link.

https://community.rapidminer.com/discussion/54621/cross-validation-and-its-outputs-in-rm-studio#latest

Best
Sara

HeikoeWin786

@Telcontar120 Thanks for your kind input. Just one more thing, I did run the cross-validation and NBC (on the same training dataset), but I get the output result (performance matrix) as the same result for both NBC and cross-validation. Is it normal? I expected different result actually.
Also, for the test dataset, I should retrieve the test dataset and use the "saved model" to test the data, correct?
I mean, how to design in RM for the test dataset? (i.e. the 25% initial unlabel data).

thanks much for your kind explanation.

Regards,
Heikoe

HeikoeWin786

@sara20
Thanks a lot Sara, I had looked into this and tried the corss-validation

Telcontar120

Cross Validation doesn't use just 25% (or any other subset) as the test set.  It iterates over all your data and uses all of it to test. I suggest you review the help documentation or the materials on RapidMiner Academy to better understand how cross validation works if it is not clear.