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This case study gives a hands-on description of Hyperparameter Tuning (HPT) methods discussed in this book. The Extreme Gradient Boosting (XGBoost) method and its implementation was chosen, because it is one of the most powerful methods in many Machine Learning (ML) tasks, especially when standard tabular data should be analyzed. This case study follows the same HPT pipeline as the first and third studies: after the data set is provided and pre-processed, the experimental design is set up. Next, the HPT experiments are performed. The R package is used as a “datascope” to analyze the results from the HPT runs from several perspectives: in addition to Classification and Regression Trees (CART), the analysis combines results from the surface, sensitivity, and parallel plots with a classical regression analysis. Severity is used to discuss the practical relevance of the results from an error-statistical point-of-view. The well-proven R package is used as a uniform interface from the methods of the packages and to the ML methods. The corresponding source code is explained in a comprehensible manner.

Similar to the example in Chap. 10, which considered tuning a Deep Neural Network (DNN), this chapter also deals with neural networks, but focuses on a different type of learning task: reinforcement learning. This increases the complexity, since any evaluation of the learning algorithm also involves the simulation of the respective environment. The learning algorithm is not just tuned with a static data set, but rather with dynamic feedback from the environment, in which an agent operates. The agent is controlled via the DNN. Also, the parameters of the reinforcement learning algorithm have to be considered in addition to the network parameters. Based on a simple example from the Keras documentation, we tune a DNN used for reinforcement learning of the inverse pendulum environment toy example. As a bonus, this chapter shows how the demonstrated tuning tools can be used to interface with and tune a learning algorithm that is implemented in Python.

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A surrogate model based Hyperparameter Tuning (HPT) approach for Deep Learning (DL) is presented. This chapter demonstrates how the architecture-level parameters (hyperparameters) of Deep Neural Networks (DNNs) that were implemented in / can be optimized. The implementation of the tuning procedure is 100% accessible from R, the software environment for statistical computing. How the software packages (, , and ) can be combined in a very efficient and effective manner will be exemplified in this chapter. The hyperparameters of a standard DNN are tuned. The performances of the six Machine Learning (ML) methods discussed in this book are compared to the results from the DNN. This study provides valuable insights in the tunability of several methods, which is of great importance for the practitioner.