The T-Digest has earned a reputation as a highly efficient and versatile sketching data structure; however, its applications as a fast generative model are less appreciated. Several common algorithms from machine learning use randomization of feature columns as a building block. Column randomization is an awkward and expensive operation when performed directly, but when implemented with generative T-Digests, it can be accomplished elegantly in a single pass that also parallelizes across Spark data partitions. In this talk Erik will review the principles of T-Digest sketching, and how T-Digests can be applied as generative models. He will explain how generative T-Digests can be used to implement fast randomization of columnar data, and conclude with demonstrations of T-Digest randomization applied to Variable Importance, Random Forest Clustering and Feature Reduction. Attendees will leave this talk with an understanding of T-Digest sketching, how T-Digests can be used as generative models, and insights into applying generative T-Digests to accelerate their own data science projects.
Session hashtag: #EUds11
Erik Erlandson is a Software Engineer at Red Hat, where he investigates analytics use cases and scalable deployments for Apache Spark in the cloud. He also consults on internal data science and analytics projects. Erik is a contributor to Apache Spark and other open source projects in the Spark ecosystem, including the Spark on Kubernetes community project, Algebird and Scala.