Problem

1. Using the concept of overfitting, explain why when a model is fit to training data, zero error with those data is not necessarily good.

2. In fitting a model to classify prospects as purchasers or non-purchasers, a certain company drew the training data from internal data that include demographic and purchase information. Future data to be classified will be lists purchased from other sources, with demographic (but not purchase) data included. It was found that "refund issued" was a useful predictor in the training data. Why is this not an appropriate variable to include in the model?