285 research outputs found
Hyperparameter Tuning Cookbook: A guide for scikit-learn, PyTorch, river, and spotPython
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
Sequential Parameter Optimization
We provide a comprehensive, effective and very efficient methodology for the design and experimental analysis of algorithms.
We rely on modern statistical techniques for tuning and understanding algorithms
from an experimental perspective. Therefore, we make use of the sequential parameter optimization (SPO) method that has been successfully applied as a tuning procedure to numerous heuristics for practical and theoretical
optimization problems.
Two case studies, which illustrate the applicability of SPO to algorithm tuning
and model selection, are presented
Beyond Particular Problem Instances: How to Create Meaningful and Generalizable Results
Computational intelligence methods have gained importance in several real-world domains such as process optimization, system identification, data mining, or statistical quality control. Tools are missing, which determine the applicability of computational intelligence methods in these application domains in an objective manner. Statistics provide methods for comparing algorithms on certain data sets. In the past, several test suites were presented and considered as state of the art. However, there are several drawbacks of these test suites, namely: (i) problem instances are somehow artificial and have no direct link to real-world settings; (ii) since there is a fixed number of test instances, algorithms can be fitted or tuned to this specific and very limited set of test functions; (iii) statistical tools for comparisons of several algorithms on several test problem instances are relatively complex and not easily to analyze. We propose a methodology to overcome these difficulties. It is based on standard ideas from statistics: analysis of variance and its extension to mixed models. This paper combines essential ideas from two approaches: problem generation and statistical analysis of computer experiments
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