I don't understand how to work with a random forest regressor after it is trained. I read and coded some tutorials about regression with random forests in Python with scikit but I don't understand how to use it in practice after it is trained.

An examlple: Let's say we have this final feature set: [name skill time location project price] and we want to make the regression for the time variable. We know the number of trees, know the depth of the trees and in training we used cross validation to prevent overfitting.

Has someone an example how to make concrete prediction with this machine learning technique?

Thanks for help and greetings

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    $\begingroup$ This is more of a practical question on how to use certain python modules, it belongs to stackoverflow. $\endgroup$ – Digio Sep 8 '18 at 9:30
  • $\begingroup$ I did also tutorials in R about Random Forest. The problem is not how to program, my problem is that I don't know what to program if the random forest has to be used. It is not about programming $\endgroup$ – ScienceLover Sep 8 '18 at 9:40
  • $\begingroup$ Just feed your input to the regressor using its method predict. Read this page, it should help: scikit-learn.org/stable/modules/generated/… $\endgroup$ – Milos Sep 8 '18 at 10:51

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