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I have a file with biological pathways and their abundance in samples. I want to create a classification random forest model in R, but aside from removing rows with null counts, I am not sure what else is necessary. I am new to this.

  1. is normalization and scaling required
  2. how can i handle confounding variables like age, sex, and weight
Jasoosa
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  • For #1, normalization and scaling doesn't do anything for a random forest model. [link](https://stackoverflow.com/questions/8961586/do-i-need-to-normalize-or-scale-data-for-randomforest-r-package). For #2, I don't think that's a question we can answer with the information provided. Confounding is only important if you want to interpret your model. You haven't described what you're hoping to figure out from interpreting your model, so we can't tell you how to deal with confounding. – Nick ODell Aug 01 '23 at 20:17
  • Also, this is more of a statistics question than a programming question. It would be better to post your question (with a concrete example of your data and code) over at [CrossValidated](https://stats.stackexchange.com), the stack site specialising in statistics questions in the same way that stack overflow specializes in programming questions. – Allan Cameron Aug 01 '23 at 20:39

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