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

Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)

Repository: https://github.com/LiYangHart/Hyperparameter-Optimization-of-Machine-Learning-Algorithms
Canonical: https://ross.abutalabs.com/products/hyperparameter-optimization-of-machine-learning-algorithms
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: hyperparameter-optimization, machine-learning-algorithms, hyperparameter-tuning, machine-learning, tuning-parameters, grid-search, random-search, optimization, hpo, bayesian-optimization, genetic-algorithm, particle-swarm-optimization, random-forest, python-examples, python-samples, deep-learning, artificial-neural-networks, machine-learning-optimization, automl, automated-machine-learning
Last push: 2022-09-22T05:24:53+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2232, "days_push": 1441, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1342, forks 303 (observed 2026-08-28T04:04:26.285879+00:00)

## What it is
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
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, benchmarking, data-science
- domain: machine-learning, artificial-intelligence, tutorials, data-science
- platform: python
- tags: hyperparameter-tuning, grid-search, random-search, bayesian-optimization, genetic-algorithm, particle-swarm-optimization, automl, jupyter-notebook, hpo

## Member repositories
- LiYangHart/Hyperparameter-Optimization-of-Machine-Learning-Algorithms (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:26.285879+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:43:04.609466+00:00, confidence not recorded.
  - readme: https://github.com/LiYangHart/Hyperparameter-Optimization-of-Machine-Learning-Algorithms (fetched 2026-08-28T04:04:26.285879+00:00, sha 2f94ae0b3183)
- Data as of 2026-08-30T08:39:29.467469+00:00.