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Recent advances in many domains require more and more complicated experimentdesign. Such complicated experiments often have many parameters, whichnecessitate parameter tuning. Tree-structured Parzen estimator (TPE), aBayesian optimization method, is widely used in recent parameter tuningframeworks. Despite its popularity, the roles of each control parameter and thealgorithm intuition have not been discussed so far. In this tutorial, we willidentify the roles of each control parameter and their impacts onhyperparameter optimization using a diverse set of benchmarks. We compare ourrecommended setting drawn from the ablation study with baseline methods anddemonstrate that our recommended setting improves the performance of TPE. OurTPE implementation is available athttps://github.com/nabenabe0928/tpe/tree/single-opt.

Tree-Structured Parzen Estimator: Understanding Its Algorithm Components and Their Roles for Better Empirical Performance