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I have some data where one of the columns is only relevant after evaluating a different column. For example, when analyzing car data to determine car values, it may be meaningful to evaluate whether a Toyota Camry is a Camry LE , and SE, or and XLE. But that is only meaningful after we determine it's a Camry first. If its a Honda, I know that those edition types are irrelevant, or perhaps even worse, counter predictive. Is there a way for me to help my RF model along by instructing it that certain decisions should always be made before sampling other columns?

I am pretty new to R, so it's taking me some time to reproduce my data. IN Addition, I was advised my mr flick that this should really be posted on the data science stack exchange. Thanks to all.

Lamden
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  • Please show a small reproducible example – akrun Feb 07 '20 at 16:40
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    It's easier to help you if you include a simple [reproducible example](https://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example) with sample input and desired output that can be used to test and verify possible solutions. – MrFlick Feb 07 '20 at 16:41
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    And this seems more like a general data modeling question than a specific programming question. If you need help modeling your data, you should ask such questions at [stats.se] or [datascience.se] instead. – MrFlick Feb 07 '20 at 16:42

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