ABSTRACT

The process of predictive modeling requires extensive feature engineering. It often involves the transformation of given feature space, typically using mathematical functions, with the objective of reducing the modeling error for a given target. However, there is no well-defined basis for performing effective feature engineering. It involves domain knowledge, intuition, and most of all, a lengthy process of trial and error. The human attention involved in overseeing this process significantly influences the cost of model generation. Moreover, when the data presented is not well described and labeled, effective manual feature engineering becomes an even more prohibitive task. In this chapter, we discuss ways to algorithmically tackle the problem of feature engineering using transformation functions in the context of supervised learning. 222