Hyperparameter Tuning Cookbook: A guide for scikit-learn, PyTorch,
  river, and spotPython
Thomas Bartz-Beielstein
This document provides a comprehensive guide to hyperparameter tuning using
spotPython for scikit-learn, PyTorch, and river. The first part introduces
spotPython's surrogate model-based optimization process, while the second part
focuses on hyperparameter tuning. Several case studies are presented, including
hyperparameter tuning for sklearn models such as Support Vector Classification,
Random Forests, Gradient Boosting (XGB), and K-nearest neighbors (KNN), as well
as a Hoeffding Adaptive Tree Regressor from river. The integration of
spotPython into the PyTorch and PyTorch Lightning training workflow is also
discussed. With a hands-on approach and step-by-step explanations, this
cookbook serves as a practical starting point for anyone interested in
hyperparameter tuning with Python. Highlights include the interplay between
Tensorboard, PyTorch Lightning, spotPython, and river. This publication is
under development, with updates available on the corresponding webpage.