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I am working on a project to determine the variables that better predict the binary outcome. I am using conditional random forest and permimp::permimp to assess the importance of variables for my subgroup analysis. Now, I want to make probability predictions from a properly constructed random forest, based on each new patient's values.

For instance, if I initially used 30 variables to fit the random forest and, through permutation, identified 7 variables as important. Now, when we have a new patient, and we want to predict the probability of their response, we may only have the 7 important variables collected, not all 30. Can we still predict the probability of the patient being a responder based on these 7 variables alone, or do we need all 30 variables? Alternatively, should we refit the random forest based only on these 7 variables?

Not all variables might be collected because the setting involves clinical data. Hence, I could train the model based on one study's data, while a clinician is presumably interested in the probability of developing the disease, but he/she might not have the same data collected.

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  • $\begingroup$ With the random forest models I've used, importance is measured continuously and predictors are not dichotomized into Important and Not. Is your type of random forest different in this regard? $\endgroup$
    – rolando2
    Jan 25 at 20:30
  • $\begingroup$ Why did you decide to use a random forest for this problem? $\endgroup$
    – dipetkov
    Jan 26 at 6:03
  • $\begingroup$ @rolando2 I am using conditional random forest and variable importance is calculated using permutations with permimp R package $\endgroup$
    – Kate
    Jan 27 at 14:20

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