Hyperparameter Optimization

Machine learning models include parameters and hyperparameters. The former refers to model coefficients that are estimated during training. The latter are parameters that are set by the user and affect how the model is fit or how it makes predictions. Setting hyperparameters manually is arduous and error-prone, instead hyperparameter optimization (HPO) automating this 'tuning' procedure to reduce bias. When performing HPO there are many considerations including what tuning algorithm to use, how long to tune it for, and what measures to optimize. Moreover users have to decide which hyperparameters to tune and for what configurations. Finally, one has to be careful to make use of nested resampling to prevent leakage of information from training to testing datasets that can occur when resampling and tuning simultaneously. This chapter begins by introducing mlr3tuning and its functionality for tuning learners. This includes Tuners for configuring and running optimization algorithms, TuningInstances for storing results, and Terminators for controlling when to stop the HPO process. The chapter provides a practical example of tuning hyperparameters of a support vector machine, including introducing logarithmic transformations. The AutoTuner class is also introduced which is used for automating nested resampling to reduce bias in tuning.