Cross Validation
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!
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Altair Employee@nicholaswongsg I just gave you a Boost badge - you should move up from Newbie and be able to post links now 
Hi @nicholaswongsg ,
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.
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.
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).
Thank you @Telcontar120 for you answer!
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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).
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.