Batch Machine Learning (BML) reaches its limits when dealing with very large
amounts of streaming data. This is especially true for available memory,
handling drift in data streams, and processing new, unknown data. Online
Machine Learning (OML) is an alternative to BML that overcomes the limitations
of BML. OML is able to process data in a sequential manner, which is especially
useful for data streams. The `river` package is a Python OML-library, which
provides a variety of online learning algorithms for classification,
regression, clustering, anomaly detection, and more. The `spotRiver` package
provides a framework for hyperparameter tuning of OML models. The
`spotRiverGUI` is a graphical user interface for the `spotRiver` package. The
`spotRiverGUI` releases the user from the burden of manually searching for the
optimal hyperparameter setting. After the data is provided, users can compare
different OML algorithms from the powerful `river` package in a convenient way
and tune the selected algorithms very efficiently.

批量机器学习在处理大量流数据时存在困难，但在线机器学习是一种解决批量机器学习限制的替代方法。river 和 spotRiver 是 Python 中的两个在线学习库，提供了分类、回归、聚类、异常检测等多种在线学习算法，并通过 spotRiverGUI 提供图形用户界面来简化用户的超参数调优过程。

在线机器学习中的超参数调整简化 -- spotRiverGUI

Simplifying Hyperparameter Tuning in Online Machine Learning -- The  spotRiverGUI

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.

本文介绍了使用 spotPython 进行超参数调整的全面指南，包括 scikit-learn、PyTorch 和 river 的应用。通过案例研究，作者展示了对 sklearn 模型（如支持向量分类、随机森林、梯度提升（XGB）和 K 最近邻（KNN））以及 river 中的 Hoeffding 自适应树回归器进行超参数调优的过程，还讨论了如何将 spotPython 集成到 PyTorch 和 PyTorch Lightning 训练工作流中。本文是一个实用的超参数调整起点，重点介绍了 Tensorboard、PyTorch Lightning、spotPython 和 river 之间的相互作用。