Prediction(Label) Result Clarification
tomolafsson
Member Posts: 1 
Contributor I
I have what I think is a relatively simple question that I can not find the answer to regarding Forecasting Performance.
I am predicting interactions we will have with our customers using previous case data as well as a number of attributes that will trigger a case.
My model seems good using Forecasting Performance, Windowing, etc.....
Here is my question. If my label data is 157 for July 30 and my Prediction(Label) for July 30 is 178, is 178 actually the prediction for the following day (July 31) or Rapidminer's prediction for July 30?
This is covered in neuralmarkettrends video #10 but is not fully clear to me. Thanks, Tom
I am predicting interactions we will have with our customers using previous case data as well as a number of attributes that will trigger a case.
My model seems good using Forecasting Performance, Windowing, etc.....
Here is my question. If my label data is 157 for July 30 and my Prediction(Label) for July 30 is 178, is 178 actually the prediction for the following day (July 31) or Rapidminer's prediction for July 30?
This is covered in neuralmarkettrends video #10 but is not fully clear to me. Thanks, Tom
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<parameter key="ODA - Devices" value="regular"/>
<parameter key="ODA - eClinicalMessenger" value="regular"/>
<parameter key="ODA - eClinicalMobile" value="regular"/>
<parameter key="ODA - eClinicalTouch" value="regular"/>
<parameter key="ODA - eClinicalWorks P2P" value="regular"/>
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0
Answers
it's the prediction of the current day+horizon.
Please note that the Windowing operators used for training and for application must be configured completely identically, which is not the case in your process - you have a different window_size and a different horizon.
Furthermore, to just train a model you don't need the X-Validation - the validation is only needed if you want to estimate the performance/quality of the model. Since you did not connect the performance output of the validation, I assume you don't need the X-Validation and can use the Linear Regression operator directly.
Best regards,
Marius