Auger

There are other tools available for this, including the open source TPOT, and commercial companies like DataRobot. TPOT claims to use genetic algorithms to exhaust the search space of algorithms and hyperparameters, but we found the performance of it closely approximated "grid search" (where all algorithms and hyperparameters are attempted successively). Auger uses a more sophisticated search approach to intelligently traverse available algorithms and hyperparameters. While we are not ready to share the details of how we do this, the suboptimality of genetic algorithms to search complex trees has been established elsewhere. One of the chief innovations we are willing to share is that we start the search of algorithms and hyperparameters with intelligent assessment of good likely values. This is done by analyzing the dataset presented. We refer to this approach as "warm start". The benefit of warm start is that it both optimizes the overall search time for the best algorithm/hyperparameter combinations, and also provides an immediate "good enough" combination that can be quickly tested (AutoML with other tools can often take hours to days to get results). As a cloud-based service, Auger continues to refine its warm start heuristics based on results from other users and datasets. Beyond just doing a "warm start" of the first attempted algorithm and options, Auger narrows down the algorithms searched based on aspects of the dataset. In many cases we can drastically reduce the search space of algorithms evaluated. With regard to commercial AutoML products, we think you will similarly find that Auger's speed to get optimal results and initially acceptable results is very favorable. In addition, Auger is an inexpensive service (free in beta now) that you should be able to evaluate without significant cost. Algorithm Implementations