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So let's say I have data with numerical variables A, B and C.

I believe that the value of A has an effect on B. I also believe that A and B both have an effect on C. I don't think C has an effect on either A or B.

I want to use machine learning to predict A, B, and C. I obviously have A and B as training data, and I have other variables as training data too.

Do I simply create multiple models to predict all three, or is there a way to make one model predict them all if I just throw the entire dataset at it?

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  • $\begingroup$ Kindly post a sample of your data. $\endgroup$
    – spectre
    Oct 16, 2021 at 8:33
  • $\begingroup$ "I want to use machine learning to predict A, B, and C. I obviously have A and B as training data." I don't understand this statement. Why do you want to predict A and B if you already have this information? I don't understand what do you want to predict, and which variables are you going to use to predict, ie: which are your features and which is your target? $\endgroup$
    – alexmolas
    Jun 27, 2022 at 10:46

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Do you have a data sample to answer better to your question?

For instance, are those variables related to time?

If yes, time series based models could be interesting like multi LSTM: LSTM Multi-class classification for large number of classes

If not, you could use a random forest regressor. https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html

My best advice is to start with a simple single model that makes predictions on A, B and C, and then try more complex ones.

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  • $\begingroup$ If you consider the answer somewhat usefull, don't hesitate to upvote it as acknowledgment :) $\endgroup$ Jul 22, 2021 at 9:21

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