Question data

You should review the RapidMiner tutorials for Cross Validation and for Apply Model. Basically you are going to define Feat 8 as the label and build your model on that, and then you are going to save that model and apply it to the 2nd dataset.

Hi @andre5007,

The workflow looks fine if you have your own test set. However, as Brian mentioned above, cross validation is always a smart option on your training set.

https://academy.rapidminer.com/learn/article/cross-validation
https://academy.rapidminer.com/learn/video/validating-a-model
https://rapidminer.com/blog/validate-models-cross-validation/

HTH!

YY

Now I noticed that I was wrong on the print I sent, because it was not the one I wanted to have selected.

I put a filter at the beginning because it had a value that was missing and because of that it gave an error.

Then in the cross validation, I placed the decision tree inside the process at the training site and in the test the apply model and performance.

Then I linked the cross validation to another apply model and in that apply model I also put the test data set where I have to define feat 8.

Do you think you should change anything in the operators parameters? Because I didn't change anything just when it was necessary to be able to run the process.

What do you think I can improve? Or if I am now on the right path? 

Thanks
Best regards

Looks like a good setup for basic model construction and validation with an additional out-of-sample validation. 

Hi @andre5007, it looks your prediction target is numerical (integers). Are you sure you want to build decision tree or any predictive model for classification, rather than regressions? I would parse the label into numbers and try the regression decision trees or GLM/GBT for regression.

Hi @yyhuang
Why do you think regression decision trees or GLM/GBT for regression is better?
Thanks
André

Hi @andre5007, my point was regression is better than classification here as the model for your data. Because the label is integer. For the difference between regression and classification, https://machinelearningmastery.com/classification-versus-regression-in-machine-learning/