Consider two logistic regression models (model A and model B) that both accept instances with one target value and the same number of independent features. The instances of model A only have numerical independent features while the instances of model B have a mixture of numerical and categorical independent features. One hot encoding is adopted where necessary, to facilitate error-based learning. Following the one hot encoding process, the inputs and outputs of both models are adjusted as necessary. Which of the following options is the most accurate with respect to the models after the one hot encoding process?