# Random Forest - Numeric and Dummy Variables together

I am trying to create a logistic regression model and a random forest model on the same data to predict probability of default. For the logistic regression model, I have created some dummy variables from categorical variables. Finally, for the input of logistic regression, I have 9 dummy variables and 2 numeric variables (age and level, age takes values from 18 to 60, level from 4 to 10). I want to use same input dataset for the random forest model. When I did so, using "randomForest" Package, I get following Variable Importance Plot.

Level seems to be a very good variable both by MSE and Node Purity. Also, level is a very important variable in logistic regression (p value ~ 10^-5). However, Age is very important by Node purity, but not by MSE. Also, in logistic regression, age is not a very good variable with p value of 0.026. So I want to understand, Does being numeric increases the node purity importance of a variable by overfitting? Is it not suitable to use numeric and dummy variables together in random forest model? Or is there something I am missing.

I had similar doubts about using numeric and dummy variables in logistic regression, but in logistic regression it did not create any problem.

• Hi, did you normalise the numerical variables? – Filippo Mazza Jun 19 '17 at 8:39

(3) Even ignoring above, the are a fair number of accepted issues with the importance measures. You might want to look into the party package that implements AUC-based importance measures as well as conditional importance measures.