Hyperparameter-Optimization-of-Machine-Learning-Algorithms
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LiYangHart
Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)
Hyperparameter Optimization of Machine Learning Algorithms
This code provides a hyper-parameter optimization implementation for machine learning algorithms, as described in the paper:
L. Yang and A. Shami, “On hyperparameter optimization of machine learning algorithms: Theory and practice,” Neurocomputing, vol. 415, pp. 295–316, 2020, doi: https://doi.org/10.1016/j.neucom.2020.07.061.
To fit a machine learning model into different problems, its hyper-parameters must be tuned. Selecting the best hyper-parameter configuration for machine learning models has a direct impact on the model's performance. In this paper, optimizing the hyper-parameters of common machine learning models is studied. We introduce several state-of-the-art optimization techniques and discuss how to apply them to machine learning algorithms. Many available libraries and frameworks developed for hyper-parameter optimization problems are provided, and some open challenges of hyper-parameter optimization research are also discussed in this paper. Moreover, experiments are conducted on benchmark datasets to compare the performance of different optimization methods and provide practical examples of hyper-parameter optimization.
This paper and code will help industrial users, data analysts, and researchers to better develop machine learning models by identifying the proper hyper-parameter configurations effectively.
Paper
On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice
One-column version: arXiv
Two-column version: Elsevier
Quick Navigation
Section 3: Important hyper-parameters of common machine learning algorithms
Section 4: Hyper-parameter optimization techniques introduction
Section 5: How to choose optimization techniques for different machine learning models
Section 6: Common Python libraries/tools for hyper-parameter optimization
Section 7: Experimental results (sample code in "HPO_Regression.ipynb" and "HPO_Classification.ipynb")
Section 8: Open challenges and future research directions
Summary table for Sections 3-6: Table 2: A comprehensive overview of common ML models, their hyper-parameters, suitable optimization techniques, and available Python libraries
Summary table for Sections 8: Table 10: The open challenges and future directions of HPO research
Implementation
Sample code for hyper-parameter optimization implementation for machine learning algorithms is provided in this repository.
Sample code for Regression problems
HPO_Regression.ipynb
Dataset used: Boston-Housing
Sample code for Classification problems
HPO_Classification.ipynb
Dataset used: MNIST
Machine Learning & Deep Learning Algorithms
- Random forest (RF)
- Support vector machine (SVM)
- K-nearest neighbor (KNN)
- Artificial Neural Networks (ANN)
Hyperparameter Configuration Space
| ML Model | Hyper-parameter | Type | Search Space |
|---|---|---|---|
| RF Classifier | n_estimators | Discrete | [10,100] |
| max_depth | Discrete | [5,50] | |
| min_samples_split | Discrete | [2,11] | |
| min_samples_leaf | Discrete | [1,11] | |
| criterion | Categorical | 'gini', 'entropy' | |
| max_features | Discrete | [1,64] | |
| SVM Classifier | C | Continuous | [0.1,50] |
| kernel | Categorical | 'linear', 'poly', 'rbf', 'sigmoid' | |
| KNN Classifier | n_neighbors | Discrete | [1,20] |
| ANN Classifier | optimizer | Categorical | 'adam', 'rmsprop', 'sgd' |
| activation | Categorical | 'relu', 'tanh' | |
| batch_size | Discrete | [16,64] | |
| neurons | Discrete | [10,100] | |
| epochs | Discrete | [20,50] | |
| patience | Discrete | [3,20] | |
| RF Regressor | n_estimators | Discrete | [10,100] |
| max_depth | Discrete | [5,50] | |
| min_samples_split | Discrete | [2,11] | |
| min_samples_leaf | Discrete | [1,11] | |
| criterion | Categorical | 'mse', 'mae' | |
| max_features | Discrete | [1,13] | |
| SVM Regressor | C | Continuous | [0.1,50] |
| kernel | Categorical | 'linear', 'poly', 'rbf', 'sigmoid' | |
| epsilon | Continuous | [0.001,1] | |
| KNN Regressor | n_neighbors | Discrete | [1,20] |
| ANN Regressor | optimizer | Categorical | 'adam', 'rmsprop' |
| activation | Categorical | 'relu', 'tanh' | |
| loss | Categorical | 'mse', 'mae' | |
| batch_size | Discrete | [16,64] | |
| neurons | Discrete | [10,100] | |
| epochs | Discrete | [20,50] | |
| patience | Discrete | [3,20] |
HPO Algorithms
- Grid search
- Random search
- Hyperband
- Bayesian Optimization with Gaussian Processes (BO-GP)
- Bayesian Optimization with Tree-structured Parzen Estimator (BO-TPE)
- Particle swarm optimization (PSO)
- Genetic algorithm (GA)
Requirements
- Python 3.5+
- Keras
- scikit-learn
- hyperband
- scikit-optimize
- hyperopt
- optunity
- DEAP
- TPOT
Contact-Info
Please feel free to contact me for any questions or cooperation opportunities. I'd be happy to help.
- Email: [email protected]
- GitHub: LiYangHart and Western OC2 Lab
- LinkedIn: Li Yang
- Google Scholar: Li Yang and OC2 Lab
Citation
If you find this repository useful in your research, please cite this article as:
L. Yang and A. Shami, “On hyperparameter optimization of machine learning algorithms: Theory and practice,” Neurocomputing, vol. 415, pp. 295–316, 2020, doi: https://doi.org/10.1016/j.neucom.2020.07.061.
@article{YANG2020295,
title = "On hyperparameter optimization of machine learning algorithms: Theory and practice",
author = "Li Yang and Abdallah Shami",
volume = "415",
pages = "295 - 316",
journal = "Neurocomputing",
year = "2020",
issn = "0925-2312",
doi = "https://doi.org/10.1016/j.neucom.2020.07.061",
url = "http://www.sciencedirect.com/science/article/pii/S0925231220311693"
}
Metadata
Owner
LiYangHart
Metadata
Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)