I have a data set with a binary variable which is 0 for 94% of observations and 1 for 6% of observations. If I fit a model (say logistic regression) to predict this variable in a way that maximizes goodness of fit over the sample then I'm liable to get a model that unconditionally predicts 0 since that predicts accurately for 94 % of the observations.
But that's not very useful for me. Theoretically both the classes are equally interesting to me. So I do a fit giving weight of 1/0.94 to each observation with value 0 and 1/0.06 to each of the observation with value 1.
Is this a sensible thing to do and does it have a standard name?