I noticed that I am getting different feature importance results with each random forest run even though they are using the same parameters. Now, I know that a random forest model takes observations randomly which is causing the importance levels to vary. This is especially shown for the less important variables.

My question is how does one interpret the variance in random forest results when running it multiple times? I know that one can reduce the instability level of results by increasing the number of trees; however, this doesn't really tell me if my feature importance results are "true" though they may be true for that specific run (but not necessarily for a separate run).

Even if I were to take an extremely large number of trees that would take a ridiculous time to compute and average the feature importance results for each variable, that still doesn't necessarily confirm that it will produce the same importance results if I repeat that exact same process again.

I also want to add that I have tried it with an extremely large number of trees and still got a slight variation in my feature importance results between runs.

Is there any method that I can use to interpret this variance of importance between runs?

The dataset contains 19705 observations with 18 variables.

Any help at all would be greatly appreciated!



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