I am trying to build a model that predicts the which binary category a respondent belongs to (0 or 1). I have demographic variables (all categorical) and a few 10 point questions.
I have built a few predictive models (just for comparison) in both R and SPSS. In SPSS I have built a Logistic Regression model, while in R I have modeled using a Decision Forests. The overall accuracy in both models is around 66%. This is seems good, however, the accuracy for correctly predicting those that are in group 1 (given those that are in group 1 and accurately predicting that they are in group 1) is only around 27%. The number of respondents in group 0 is larger than those in group 1 which is the reason why there is a significant difference between the two accuracies (66% and 27%).
In the data set, about 37% of the sample makes up group 1. I'm wondering if there is some way to increase the 27%, as the model is currently useless (i.e. I need to know more accurately those in group 1). Or is there another method of modeling that I should be using?
Any help at all would be greatly appreciated!