Easy Hyperparameter Search Using Optunity
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Abstract (arXiv)
Optunity is a free software package dedicated to hyperparameter optimization. It contains various types of solvers, ranging from undirected methods to direct search, particle swarm and evolutionary optimization. The design focuses on ease of use, flexibility, code clarity and interoperability with existing software in all machine learning environments. Optunity is written in Python and contains interfaces to environments such as R and MATLAB. Optunity uses a BSD license and is freely available online at http://www.optunity.net.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Dec 2014.
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Citing papers per year: 2021: 3; 2022: 3; 2023: 2; 2024: 3.
11 papers.
- 2021How Much Automation Does a Data Scientist Want?cited by 20
- 2022A Comparative study of Hyper-Parameter Optimization Toolscited by 10
- 2022An Attention-based ConvLSTM Autoencoder with Dynamic Thresholding for Unsupervised Anomaly Detection in Multivariate Time Seriescited by 9
- 2021Search Algorithms for Automated Hyper-Parameter Tuningcited by 8
- 2021VisRuler: Visual Analytics for Extracting Decision Rules from Bagged and Boosted Decision Treescited by 8
- 2024X Hacking: The Threat of Misguided AutoMLcited by 5
- 2023ASP: Automatic Selection of Proxy dataset for efficient AutoMLcited by 3
- 2024Advancing Machine Learning in Industry 4.0: Benchmark Framework for Rare-event Prediction in Chemical Processescited by 1
- 2024Experimental Evaluation of Machine Learning Models for Goal-oriented Customer Service Chatbot with Pipeline Architecturecited by 1
- 2022Random Search Hyper-Parameter Tuning: Expected Improvement Estimation and the Corresponding Lower Boundcited by 0
- 2023AutoEn: An AutoML method based on ensembles of predefined Machine Learning pipelines for supervised Traffic Forecastingcited by 0
- 2024Experimental Evaluation of Machine Learning Models for Goal-oriented Customer Service Chatbot with Pipeline Architecture27 Sep 2024
- 2024Advancing Machine Learning in Industry 4.0: Benchmark Framework for Rare-event Prediction in Chemical Processes31 Aug 2024
- 2024X Hacking: The Threat of Misguided AutoML16 Jan 2024
- 2023ASP: Automatic Selection of Proxy dataset for efficient AutoML17 Oct 2023
- 2023AutoEn: An AutoML method based on ensembles of predefined Machine Learning pipelines for supervised Traffic Forecasting19 Mar 2023
- 2022Random Search Hyper-Parameter Tuning: Expected Improvement Estimation and the Corresponding Lower Bound17 Aug 2022
- 2022An Attention-based ConvLSTM Autoencoder with Dynamic Thresholding for Unsupervised Anomaly Detection in Multivariate Time Series23 Jan 2022
- 2022A Comparative study of Hyper-Parameter Optimization Tools17 Jan 2022
- 2021VisRuler: Visual Analytics for Extracting Decision Rules from Bagged and Boosted Decision Trees1 Dec 2021
- 2021Search Algorithms for Automated Hyper-Parameter Tuning29 Apr 2021
- 2021How Much Automation Does a Data Scientist Want?7 Jan 2021