Abstract
In this paper, we present a method for constructing a feature-engineered random forest by transforming the features of a given data set using a set of diverse and randomized transforms. The transformed features are then used for creating splits at each node of a random forest. In particular, we use sum-product features because of their strong expressive power. This type of on-the-fly feature engineering has significant advantages over traditional random forests because it adds to the diversity of the splits. Such a diversity further helps in variance reduction; over and above the variance reduction ability offered by traditional random forests. We show the advantages of the proposed approach over traditional random forests and other well-established baselines using extensive experimental evaluation.