  Machine learning techniques applied to software engineering tasks can be
improved by hyperparameter optimization, i.e., automatic tools that find good
settings for a learner's control parameters.
  We show that such hyperparameter optimization can be unnecessarily slow,
particularly when the optimizers waste time exploring "redundant tunings"',
i.e., pairs of tunings which lead to indistinguishable results. By ignoring
redundant tunings, DODGE, a tuning tool, runs orders of magnitude faster, while
also generating learners with more accurate predictions than seen in prior
state-of-the-art approaches.
