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I am running several random forest regression models on different datasets. In each, I have a continuous DV and ~30 dichotomous predictors. I don't expect these predictors to explain much variance. What I am really interested in is which ones are related to the dependent variable.

In some datasets, the model predicts ~5% of the variance, which is about what I would expect. But in others, it is < 1% and is sometimes negative.

This made me wonder if there is a certain minimum explained variance, below which the importance of predictors in a model shouldn't be interpreted?

Dave
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    How do you measure explained variance? – Dave Nov 04 '21 at 00:04
  • @Dave I'm using the randomForests package, and part of the output is a "% of variance explained". I believe it is an R2. – Dave Nov 04 '21 at 01:32
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    (Nice username) [As a heads up, $R^2$ for nonlinear regression models like random forests does not correspond to the “percentage of variance explained” that it does in the linear case.](https://stats.stackexchange.com/a/547870/247274) – Dave Nov 04 '21 at 01:41

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