This code provides a simple HPO implementation (Grid and Random search) for machine learning models, as described in the paper "Search Algorithms for AutomatedHyper-Parameter Tuning in Machine Learning".

This paper will help users to improve their machine learning models by optimizing their models' hyper-parameter automatically.

Search Algorithms for Automated Hyper-Parameter Tuning in Machine Learning paper link

Sample code for hyper-parameter optimization implementation for machine learning algorithms is provided in this repository.

• Grid-Classification

• Random-Classification

Dataset used: MIDFIELD

• Naive Bayes (NB)
• Logistic Regression (LR)
• Decision Tree (DT)
• Random forest (RF)
• Support vector machine (SVM)
• K-nearest neighbor (KNN)
• Extreme Gradient Boosting (XGBoost)

• Grid search
• Random search

• Python 3.7
• scikit-learn

Please dont hesitate to contact me:

• Email: [email protected]
• GitHub: LeilaZahedi
• LinkedIn: Leila Zahedi

If this codes helped you in your research, please cite the article:

L. Zahedi, F. G. Mohammadi, S. Rezapour, M. W. Ohland, and M. H.Amini, “Search algorithms for automated hyper-parameter tuning,”The 17th International Conference on Data Science, 2021.