I want to develop a predictive model based on a binary dependent variable (1 if default, 0 otherwise). Based on methods like logistic regression, decision trees, and random forest in R.
My independent variables consist of:
- Multiple binary variables
- One categorical variable, which contains 10 different categories. This variable is split to 10 new dummy variables, where 9 is included in the model.
- Other variables are integer numbers e.g., year, etc. Q1: When I develop these models in R, what type of structure should the variables be? I have seen that in decision trees and random forest, many use “factor” for the classification dependent variable. Is this correct for binary? And what should be used for the independent variables?
Q2: When evaluating the models, which performance metric is the best choice to use in this case? Accuracy, AUC, F1 score, etc.