Classification and regression tree learning on massive datasets is a common data mining task at Google, yet many state of the art tree learning algorithms require training data to reside in memory on a single machine. While more scalable implementations of tree learning have been proposed, they typically require specialized parallel computing architectures. In contrast, the majority of Google’s...

Cross Validation (CV) is widely used to measure the performance of a classifier. The main purpose of this study is to explore the behavior of CV in tree-based models. We report experimental studies that compare a cross-validated tree classifier with an oracle classifier that is ideally derived on the knowledge of underlying distributions. The main observation of this study indicates that the di...

Previously, researchers who developed representative driving cycles mainly focused on light-duty vehicles and only considered vehicle speed related derivations. In this paper, we propose a novel approach to develop for heavy-duty vehicles. By implementing decision tree regression (DTR) the Fleet DNA on-road data, broader set of metrics, such as engine power fuel consumption, can be used more ro...