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Hi sgenzer, thanks for your post. I tried already the Linear Regression operator but my problem is, that I need the Linear regression for a several set of countries. So p.e. I need the Linear Regression for Italy, Spain and Portugal (first set) and second set France, Netherlands, Germany (second set) etc. etc.
The linear regression operator works only with numerical numbers, which means I have to delete the column of the country names, so then I can not set different sets. In the result I have just one regression function for all the data / all the countries...
Instead of deleting the country attribute, you can do this simply by first filtering for the value of each country one at a time, running the regression, and then capturing the associated linear regression model. You could do this manually, or you could actually automate it easily with a Loop operator in RapidMiner (filter for each country value, build a regression, and save the model).
hi @Carlo - ok a lot going on here. Perhaps you just want to use a Loop Attributes operator? You can then select the subset (countries) to loop, and then use Select Attributes and your Linear Regression inside the Loop Attributes operator. That will likely do the trick.
Scott
Scott
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Instead of deleting the country attribute, you can do this simply by first filtering for the value of each country one at a time, running the regression, and then capturing the associated linear regression model. You could do this manually, or you could actually automate it easily with a Loop operator in RapidMiner (filter for each country value, build a regression, and save the model).
hi @Carlo - ok a lot going on here. Perhaps you just want to use a Loop Attributes operator? You can then select the subset (countries) to loop, and then use Select Attributes and your Linear Regression inside the Loop Attributes operator. That will likely do the trick.
Scott
Scott
Currently that is not possible, but for linear regression in Scatter plots the function will be shown in the tooltip with the next patch (9.2.1).
Regards,
Marco