Hyperparameter Tuning Cookbook: A guide for scikit-learn, PyTorch, river, and spotPython
It offers a hands-on tutorial for practitioners in machine learning, but is incremental as it compiles existing tools without introducing new methods.
The paper provides a practical guide for hyperparameter tuning using spotPython across scikit-learn, PyTorch, and river, with case studies on models like Support Vector Classification and Random Forests, but does not report specific performance improvements or numbers.
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.