Treffer: MetaSklearn: A Metaheuristic-Powered Hyperparameter Optimization Framework for Scikit-Learn Models
Title:
MetaSklearn: A Metaheuristic-Powered Hyperparameter Optimization Framework for Scikit-Learn Models
Authors:
Publication Year:
2025
Subject Terms:
Evolutionary computation, Modelling and simulation, Information systems development methodologies and practice, Neural networks, Machine learning not elsewhere classified, Automated software engineering, Software architecture, Other information and computing sciences not elsewhere classified, Hyperparameter Tuning, Scikit-Learn, Metaheuristic Algorithms, Nature-Inspired Optimization
Document Type:
E-Ressource
software
Language:
unknown
DOI:
10.6084/m9.figshare.28978805.v1
Availability:
Rights:
GPL 3.0+
Accession Number:
edsbas.DED11D28
Database:
BASE
Weitere Informationen
**MetaSklearn** is a flexible and extensible Python library that brings metaheuristic optimization to hyperparameter tuning of scikit-learn models. It provides a seamless interface to optimize hyperparameters using nature-inspired algorithms from the Mealpy library. It is designed to be user-friendly and efficient, making it easy to integrate into your machine learning workflow.