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Several recently devised machine learning (ML) algorithms have shown improvedaccuracy for various predictive problems. Model searches, which explore to findan optimal ML algorithm and hyperparameter values for the target problem, playa critical role in such improvements. During a model search, data scientiststypically use multiple ML implementations to construct several predictivemodels; however, it takes significant time and effort to employ multiple MLimplementations due to the need to learn how to use them, prepare input data inseveral different formats, and compare their outputs. Our proposed frameworkaddresses these issues by providing simple and unified coding method. It hasbeen designed with the following two attractive features: i) new machinelearning implementations can be added easily via common interfaces between theframework and ML implementations and ii) it can be scaled to handle large modelconfiguration search spaces via profile-based scheduling. The results of ourevaluation indicate that, with our framework, implementers need only write55-144 lines of code to add a new ML implementation. They also show that ourswas the fastest framework for the HIGGS dataset, and the second-fastest for theSECOM dataset.

A Framework for Model Search Across Multiple Machine Learning Implementations