Hyperparameter Optimization of Machine Learning Algorithms

This code provides a hyper-parameter optimization implementation for machine learning algorithms, as described in the paper "On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice".

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 "HPORegression.ipynb" and "HPOClassification.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 | nestimators | Discrete | [10,100] | | | maxdepth | Discrete | [5,50] | | | minsamplessplit | Discrete | [2,11] | | | minsamplesleaf | Discrete | [1,11] | | | criterion | Categorical | 'gini', 'entropy' | | | maxfeatures | Discrete | [1,64] | | SVM Classifier | C | Continuous | [0.1,50] | | | kernel | Categorical | 'linear', 'poly', 'rbf', 'sigmoid' | | KNN Classifier | nneighbors | Discrete | [1,20] | | ANN Classifier | optimizer | Categorical | 'adam', 'rmsprop', 'sgd' | | | activation | Categorical | 'relu', 'tanh' | | | batchsize | Discrete | [16,64] | | | neurons | Discrete | [10,100] | | | epochs | Discrete | [20,50] | | | patience | Discrete | [3,20] | | RF Regressor | nestimators | Discrete | [10,100] | | | maxdepth | Discrete | [5,50] | | | minsamplessplit | Discrete | [2,11] | | | minsamplesleaf | Discrete | [1,11] | | | criterion | Categorical | 'mse', 'mae' | | | maxfeatures | Discrete | [1,13] | | SVM Regressor | C | Continuous | [0.1,50] | | | kernel | Categorical | 'linear', 'poly', 'rbf', 'sigmoid' | | | epsilon | Continuous | [0.001,1] | | KNN Regressor | nneighbors | Discrete | [1,20] | | ANN Regressor | optimizer | Categorical | 'adam', 'rmsprop' | | | activation | Categorical | 'relu', 'tanh' | | | loss | Categorical | 'mse', 'mae' | | | batchsize | 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"
}