Due to recent updates, all users are required to create an Altair One account to login to the RapidMiner community. Click the Register button to create your account using the same email that you have previously used to login to the RapidMiner community. This will ensure that any previously created content will be synced to your Altair One account. Once you login, you will be asked to provide a username that identifies you to other Community users. Email us at Community with questions.
Cross Validation
Learner III
Hi everyone,
I am using different regression models to predict electricity consumption. To evaluate my models I use cross validation so I have process and subprocess. How can I see the predictions my models make? I mean where should I place the "Explain Prediction" operator?
and For Randm Forest, where should I place the " Weights by Tree Importance" operator to have an idea about the prediction power of the attributes.
This would be very helpful, thank you in advance!
I am using different regression models to predict electricity consumption. To evaluate my models I use cross validation so I have process and subprocess. How can I see the predictions my models make? I mean where should I place the "Explain Prediction" operator?
and For Randm Forest, where should I place the " Weights by Tree Importance" operator to have an idea about the prediction power of the attributes.
This would be very helpful, thank you in advance!
Tagged:
0
Best Answer
-
Telcontar120
RapidMiner Certified Analyst, RapidMiner Certified Expert, Member Posts: 1,635 
Unicorn
In both cases, you should put the operators outside the Cross Validation. If you want to see the predictions on your full dataset, you should connect the "Test" ouput port from the right-hand side of the inner cross-validation, and then pull that through on the outside. That will give you your fully scored exampleset (and you will be able to use the operators you mention above).
5
Answers
Hey Islem_h! Your project really sound exciting. Cross validation operator is a really powerful tool! It can be used to estimate the statistical performance of a learning model. Below I have set up a project with cross validation in place which you can take a look at it.
I have uploaded the image to my drive because I'm facing an error while uploading it here. Probably because I just joined. drive(dot)google(dot)com/open?id=1B11WunE16eQxLWD_6CAbqG_9F1jE0I0w
Hope this help with what you want to achieve.
the project you shared shows only a simple example of where to place the cross validation operator.
My question was rather, in the case I am using cross validation which means I have a process and a subprocess: How can I see the predictions my models make? I mean where should I place the "Explain Prediction" operator?
and For Randm Forest, where should I place the " Weights by Tree Importance" operator to have an idea about the prediction power of the attributes.