Making use of attributes present only in the training data. • /r/MachineLearning

Suppose your learning algorithm has the following form: 1. learn a transformation of the always-present attributes, 2. learn a prediction rule as a function of the transformed features. Alternatively, if you're doing variable selection, you can think of choosing the relevant variables as the transformation). You can use additional information that is only present in the training data to assist with the first step. For example, if a feature transformation is useful for predicting not only whether the email was opened or not, but also the other training-only features, then it is likely more meaningful than a transformation that is only useful for predicting whether the email was opened. One way you might exploit this intuition algorithmically is to just apply your algorithm to learn a predictor for all of the features that you don't have in the testing data (i.e., a single vector-valued predictor that predicts if the email was opened or not, together with all of the other training-only attributes).