Hyperparameter-Optimization-of-Machine-Learning-Algorithms
Implementation of hyperparameter optimization methods for ML/DL models with sample code for regression and classification tasks.
What is Hyperparameter-Optimization-of-Machine-Learning-Algorithms?
Hyperparameter Optimization of Machine Learning Algorithms is a code repository that implements various hyperparameter optimization techniques for tuning machine learning and deep learning models. It provides practical examples and sample code to help users efficiently find optimal hyperparameter configurations for models like Random Forest, SVM, KNN, and Artificial Neural Networks. The implementation is based on a research paper that covers both theoretical foundations and practical applications of HPO methods.
Data scientists, machine learning engineers, researchers, and students who need to optimize hyperparameters for ML/DL models in their projects. Industrial users and analysts looking to improve model performance through systematic tuning will also benefit.
It offers a comprehensive, research-backed collection of HPO implementations with ready-to-use notebooks, covering multiple optimization algorithms and model types. Unlike generic tutorials, it provides specific configuration spaces and practical examples that bridge theory with immediate application.
Overview
Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)
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