fits a logistic regression model to the Weight of Evidence (WOE) data and stores the model predictor names and corresponding coefficients in the creditscorecard object.fitmodel internally transforms all the predictor variables into WOE values, using the bins found with the automatic or manual binning process. The response variable is mapped so that "Good" is 1, and "Bad" is 0. This implies that higher (unscaled) scores correspond to better (less risky) individuals (smaller probability of default).Alternatively, you can use setmodel to provide names of the predictors that you want in the logistic regression model, along with their corresponding coefficients.