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ORCIDHyperparameter Tuning for Machine and Deep Learning with R, 2023
- Eva Bartz, Thomas Bartz-Beielstein
, Martin Zaefferer, Olaf Mersmann:
Hyperparameter Tuning for Machine and Deep Learning with R - A Practical Guide. Springer 2023, ISBN 978-981-19-5169-5 - Eva Bartz:
Introduction. 1-4
Theory
- Thomas Bartz-Beielstein, Martin Zaefferer, Olaf Mersmann:
Tuning: Methodology. 7-26 - Thomas Bartz-Beielstein, Martin Zaefferer:
Models. 27-69 - Thomas Bartz-Beielstein, Martin Zaefferer:
Hyperparameter Tuning Approaches. 71-119 - Thomas Bartz-Beielstein, Olaf Mersmann, Sowmya Chandrasekaran:
Ranking and Result Aggregation. 121-161
Applications
- Thomas Bartz-Beielstein:
Hyperparameter Tuning and Optimization Applications. 165-175 - Florian Dumpert, Elena Schmidt:
Hyperparameter Tuning in German Official Statistics. 177-185 - Thomas Bartz-Beielstein, Sowmya Chandrasekaran, Frederik Rehbach, Martin Zaefferer:
Case Study I: Tuning Random Forest (Ranger). 187-220 - Thomas Bartz-Beielstein, Sowmya Chandrasekaran, Frederik Rehbach:
Case Study II: Tuning of Gradient Boosting (xgboost). 221-234 - Thomas Bartz-Beielstein, Sowmya Chandrasekaran, Frederik Rehbach:
Case Study III: Tuning of Deep Neural Networks. 235-269 - Martin Zaefferer, Sowmya Chandrasekaran:
Case Study IV: Tuned Reinforcement Learning (in Python). 271-281 - Martin Zaefferer, Olaf Mersmann, Thomas Bartz-Beielstein:
Global Study: Influence of Tuning. 283-301
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