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Similar questions have been asked on these two posts: random forest how to use the results and How to use random forest for regression after it is trained but I feel like the replies didn't give proper answers.

In python, I was able to use a random forest regression to predict a variable and the test data was quite positive. So now I'm interested in using it for later usages and well… how do I do that exactly?

Unlike a linear regression model where you just get the coefficients and you build a new model with these coefficients, how do you build a new model with the random forest? When I build this new code (that will be use by others), do I have to add the initial training data that I had in order to predict new incoming outputs? It seems a bit silly to do, no?

Thank you!

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    $\begingroup$ Why would you have to build a new linear regression model? As long as the model object is somewhere, you can call predict on it. Are you asking how to persist and distribute python objects? $\endgroup$
    – Sycorax ♦
    Commented Nov 28, 2018 at 15:55
  • $\begingroup$ @Sycorax Yes exactly! I was looking on how this process should be done. So it's related to the language i'm using. I'll check that out. Thx $\endgroup$
    – MorningGlory
    Commented Nov 28, 2018 at 17:17

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Assuming you are using sklearn to train the Model, you could use pythons pickle module to save and load the model. See this link for an example.

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