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Machine Learning (ML) algorithms have been increasingly applied to problemsfrom several different areas. Despite their growing popularity, theirpredictive performance is usually affected by the values assigned to theirhyperparameters (HPs). As consequence, researchers and practitioners face thechallenge of how to set these values. Many users have limited knowledge aboutML algorithms and the effect of their HP values and, therefore, do not takeadvantage of suitable settings. They usually define the HP values by trial anderror, which is very subjective, not guaranteed to find good values anddependent on the user experience. Tuning techniques search for HP values ableto maximize the predictive performance of induced models for a given dataset,but have the drawback of a high computational cost. Thus, practitioners usedefault values suggested by the algorithm developer or by tools implementingthe algorithm. Although default values usually result in models with acceptablepredictive performance, different implementations of the same algorithm cansuggest distinct default values. To maintain a balance between tuning and usingdefault values, we propose a strategy to generate new optimized default values.Our approach is grounded on a small set of optimized values able to obtainpredictive performance values better than default settings provided by populartools. After performing a large experiment and a careful analysis of theresults, we concluded that our approach delivers better default values.Besides, it leads to competitive solutions when compared to tuned values,making it easier to use and having a lower cost. We also extracted simple rulesto guide practitioners in deciding whether to use our new methodology or a HPtuning approach.

Rethinking Default Values: a Low Cost and Efficient Strategy to Define Hyperparameters