Re: [R] Predictor Importance in Random Forests and bootstrap

I think that the fundamental problem is that you are using the default value of ntree (500). You should always use at least 1500 and more if n or p are large. Also, this link will give you more up-to-date information on that package and feature selection: http://caret.r-forge.r-project.org/featur

Re: [R] Predictor Importance in Random Forests and bootstrap

Here is a great response I got from SO: There is an important difference between the two importance measures: MeanDecreaseAccuracy is calculated using out of bag (OOB) data, MeanDecreaseGini is not. For each tree MeanDecreaseAccuracy is calculated on observations not used to form that particular t

Re: [R] Predictor Importance in Random Forests and bootstrap

Thank you, Bert. I'll definitely ask there. In the meantime I just wanted to ensure that my R code (my function for bootstrap and the bootstrap run) is correct and my abnormal bootstrap results are not a function of my erroneous code. Thank you! On Mon, Jan 27, 2014 at 7:09 PM, Bert Gunter wrot

Re: [R] Predictor Importance in Random Forests and bootstrap

I **think** this kind of methodological issue might be better at SO (stats.stackexchange.com). It's not really about R programming, which is the main focus of this list. And yes, I know they do intersect. Nevertheless... Cheers, Bert Bert Gunter Genentech Nonclinical Biostatistics (650) 467-7374

[R] Predictor Importance in Random Forests and bootstrap

Hello! Below, I: 1. Create a data set with a bunch of factors. All of them are predictors and 'y' is the dependent variable. 2. I run a classification Random Forests run with predictor importance. I look at 2 measures of importance - MeanDecreaseAccuracy and MeanDecreaseGini 3. I run 2 boostrap run