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performance of testing data
Best Answer
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lionelderkrikor RapidMiner Certified Analyst, Member Posts: 1,195 UnicornNo, You can't !
Regards,
Lionel6
Answers
Your process is correct. Your performance vector is given by the ave output port of the Validation operator.
Do you encounter any error with this process ?
Regards,
Lionel
Just add another Performance operator after the Apply Model (2) which will then calculate the error rates for the provided test data.
The cross-validated error and the test error should be similar (provided you have enough data and it follows the same distributions).
Hope this helps,
Ingo
What Ingo said means that you have to apply strictly the same preprocessing steps to both your training dataset and test dataset.
From your screenshot of your previous post, it seems that your are selecting only some features (via the Weight by Information Gain / Select by Weights operators) during your training step.
You have to apply strictly the same selection to your test data.
To have a personalized response, please share your process(es) and all your dataset(s).
Regards,
Lionel
In attached file, the working process.
I'm able to obtain a test performance (accuracy) of around 70 % (calculated by the Cross Validation operator).
Hope this helps,
Regards,
Lionel
PS : You can not calculate the "test error" from your dataset "testing data2.1" because you have not the true label...