https://github.com/solegalli/hyperparameter-optimization

Code repository for the online course Hyperparameter Optimization for Machine Learning
https://github.com/solegalli/hyperparameter-optimization

data-science hyperopt hyperparameter-optimization machine-learning optuna python scikit-optimize

Last synced: 14 days ago
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Code repository for the online course Hyperparameter Optimization for Machine Learning

• Host: GitHub
• URL: https://github.com/solegalli/hyperparameter-optimization
• Owner: solegalli
• License: other
• Created: 2021-01-27T14:04:05.000Z (almost 4 years ago)
• Default Branch: master
• Last Pushed: 2024-09-24T12:35:55.000Z (about 2 months ago)
• Last Synced: 2024-10-23T07:53:58.811Z (21 days ago)
• Topics: data-science, hyperopt, hyperparameter-optimization, machine-learning, optuna, python, scikit-optimize
• Language: Jupyter Notebook
• Homepage: https://www.courses.trainindata.com/p/hyperparameter-optimization-for-machine-learning
• Size: 10.4 MB
• Stars: 112
• Watchers: 1
• Forks: 186
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Funding: .github/FUNDING.yml
  • License: LICENSE

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README

        
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## Hyperparameter tuning for Machine Learning - Code Repository

[](https://www.trainindata.com/p/hyperparameter-optimization-for-machine-learning)

**Launched**: May, 2021

**Updated**: September, 2024

Actively maintained.

[](https://www.trainindata.com/p/hyperparameter-optimization-for-machine-learning)

## Links

- [Online Course](https://www.trainindata.com/p/hyperparameter-optimization-for-machine-learning)

## Table of Contents

1. **Metrics**

	1. Classification (accuracy, precision, recall, roc-auc, etc)

	2. Regression (MSE, RMSE, R2, etc)

2. **Cross-Validation**

	1. K-fold, LOOCV, LPOCV, Stratified CV

	2. Group CV and variants

	3. CV for time series

	4. Nested CV

3. **Basic Search Algorithms**

	1. Manual Search 

	2. Grid Search

	3. Random Search

4. **Bayesian Optimization**

	1. with Gaussian Processes

	2. with Random Forests (SMAC) and GBMs

	3. with Parzen windows (Tree-structured Parzen Estimators or TPE)

	4. Simulated annealing

5. **Multi-fidelity Optimization**

	1. Successive Halving

	2. Hyperband

6. **Python tools**

	1. Scikit-learn

	2. Scikit-optimize

	3. Hyperopt

	4. Optuna

## Links

- [Online Course](https://www.trainindata.com/p/hyperparameter-optimization-for-machine-learning)