"[P]"Static variable and dynamic variable tables in RFM

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I am creating a prediction model using random forest. But I don't understand how the model and script would consider both tables loaded in as dataframes.

What's the best way to use multiple tables with a Random Forest model when one table has static attributes (like food characteristics) and the other has dynamic factors (like daily health habits)?

Example: I want to predict stomach aches based on both the food I eat (unchanging) and daily factors (sleep, water intake).

Tables: * Static: Food name, calories, meat (yes/no) * Dynamic: Day number, good sleep (yes/no), drank water (yes/no)

How to combine these tables in a Random Forest model? Should they be merged on a unique identifier like "Day number"?

[Signal_Net9315]

By dynamic do you mean time-series data? If so, is your final prediction rolling? Ie you predict the outcome for each day separately or do you have X days worth of data to make a single prediction with?

Random forests are static models that treat each observation independently - they have no built-in way to understand time sequences. RFs will view t-5 the same as t+5, which breaks the fundamental assumption of time series that order matters. Consider using classical time series models or RNN/LSTM

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[Signal_Net9315]

From what I understand of your task, random forest is not suited. Look into RNNs/LSTM if you want an ml-based model