I have a categorical variable with N factor levels (e.g. gender has two levels) in binary classification problem. I have converted it into dummy variables (male and female).
I have to use neural network (nnet) to classify. I have two options -
- Include any N-1 dummy variables in the input data (e.g. include either male or female). In statistical models, we use N-1 dummy variables.
- Include all N dummy variables (e.g. include both male and female)
Can someone please highlight the pros and cons of both options in predictive power and interpretability