LiYangHart/Hyperparameter-Optimization-of-Machine-Learning-Algorithms resource

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1342 stars · 303 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A collection of Jupyter Notebook examples implementing hyperparameter optimization techniques (grid search, random search, Bayesian optimization, genetic algorithms, PSO) for machine learning and deep learning models. It accompanies a published Neurocomputing survey paper and serves as an educational reference with benchmark experiments.

Use cases

• learn hyperparameter tuning methods for machine learning models
• compare grid search vs random search vs Bayesian optimization
• find example code for tuning random forest hyperparameters
• understand which HPO technique fits which ML model
• study hyperparameter optimization from a survey paper with code
• get started with AutoML and hyperparameter optimization in Python

When to choose

• you want clear, educational example notebooks for HPO methods
• you need reference implementations tied to a peer-reviewed paper
• you are learning or teaching hyperparameter optimization concepts

When to avoid

• you need a production-grade HPO framework or library
• you require actively maintained tooling with recent updates
• you need full AutoML pipelines rather than tuning examples

Facets

learning-resource · maturity maintenance

machine-learning benchmarking data-science machine-learning artificial-intelligence tutorials data-science python hyperparameter-tuning grid-search random-search bayesian-optimization genetic-algorithm particle-swarm-optimization automl jupyter-notebook hpo

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