Abstract is missing.
- IntroductionEva Bartz. 1-4 [doi]
- Tuning: MethodologyThomas Bartz-Beielstein, Martin Zaefferer, Olaf Mersmann. 7-26 [doi]
- ModelsThomas Bartz-Beielstein, Martin Zaefferer. 27-69 [doi]
- Hyperparameter Tuning ApproachesThomas Bartz-Beielstein, Martin Zaefferer. 71-119 [doi]
- Ranking and Result AggregationThomas Bartz-Beielstein, Olaf Mersmann, Sowmya Chandrasekaran. 121-161 [doi]
- Hyperparameter Tuning and Optimization ApplicationsThomas Bartz-Beielstein. 165-175 [doi]
- Hyperparameter Tuning in German Official StatisticsFlorian Dumpert, Elena Schmidt. 177-185 [doi]
- Case Study I: Tuning Random Forest (Ranger)Thomas Bartz-Beielstein, Sowmya Chandrasekaran, Frederik Rehbach, Martin Zaefferer. 187-220 [doi]
- Case Study II: Tuning of Gradient Boosting (xgboost)Thomas Bartz-Beielstein, Sowmya Chandrasekaran, Frederik Rehbach. 221-234 [doi]
- Case Study III: Tuning of Deep Neural NetworksThomas Bartz-Beielstein, Sowmya Chandrasekaran, Frederik Rehbach. 235-269 [doi]
- Case Study IV: Tuned Reinforcement Learning (in Python)Martin Zaefferer, Sowmya Chandrasekaran. 271-281 [doi]
- Global Study: Influence of TuningMartin Zaefferer, Olaf Mersmann, Thomas Bartz-Beielstein. 283-301 [doi]