text: abstract
  machine learning (ml) has revamped every domain of life as it
  provides powerful tools to build complex systems that learn and
  improve from experience and data. our key insight is that to solve
  a machine learning problem, data scientists do not invent a new
  algorithm each time, but evaluate a range of existing models with
  different configurations and select the best one. this task is laborious, error-prone, and drains a large chunk of project budget and
  time. in this paper we present a novel framework inspired by programming by sketching[8] and partial evaluation[4] to minimize
  human intervention in developing ml solutions. we templatize
  machine learning algorithms to expose configuration choices as
  holes to be searched. we share code and computation between different algorithms, and only partially evaluate configuration space
  of algorithms based on information gained from initial algorithm
  evaluations. we also employ hierarchical and heuristic based pruning to reduce the search space. our initial findings indicate that our
  approach can generate highly accurate ml models. interviews with
  data scientists show that they feel our framework can eliminate
  sources of common errors and significantly reduce development
  time.