Human-in-the-Loop Feature Discovery for Tabular Data

In recent years, researchers have developed several methods to automate discovering datasets and augmenting features for training Machine Learning (ML) models. Together with feature selection, these efforts have paved the way towards what is termed the feature discovery process. Data scientists and engineers use automated feature discovery over tabular datasets to add new features from different sources and enrich training data. By surveying data practitioners, we have observed that automated feature discovery approaches do not allow data scientists to use their domain knowledge during the feature discovery process. In addition, automated feature discovery methods can leak private features or introduce biased ones. In this paper, we introduce the first user-driven human-in-the-loop feature discovery method called HILAutoFeat. We demonstrate the capabilities of HILAutoFeat, which effectively combines automated feature discovery with user-driven insights. Our demonstration is centred around two scenarios: (i) an automated feature discovery scenario - HILAutoFeat acts as a steward in a large data lake where the user is unaware of the quality and relevance of the data, and (ii) a scenario where HILAutoFeat and the user work together - the user drives the feature discovery process by adding his domain and business knowledge, while HILAutoFeat performs the intensive computations.