Suppose I have 5 predictor variables, a binary response variable, and 100 data points. I want to try to predict what the chance of the binary response being -1 or 1 is, but 99 of the response values are -1.
What is the best way to go about making predictions on this data set (with heavily 'skewed' responses) without collecting additional data?
To clarify, it is clear that a model fitted to this data will probably do a bad job classifying new data. Can we, for example, make up data in order to improve the results? What are some other ways to try to get a better idea of what the data might look like at 1000 samples?