# Optuna

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Hyperparameter optimization framework

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Optuna Developer Preferred Networks Release Version 1.0 (January, 2020) Stable release Version 4.4.0 (June 16, 2025) Written in Python Available in English License MIT License Website optuna.org Repository github.com/optuna/optuna

**Optuna** is an [open-source](/source/Open_source) [Python](/source/Python_(programming_language)) library for automatic [hyperparameter](/source/Hyperparameter_(machine_learning)) tuning of [machine learning](/source/Machine_learning) models.[1] It was first introduced in 2018 by [Preferred Networks](/source/Preferred_Networks), a Japanese startup that works on practical applications of [deep learning](/source/Deep_learning) in various fields.[2][3][4][5] The beta version of Optuna was released at the end of the year, with the subsequent first major stable release announced in January 2020.[1]

## Hyperparameter optimization

See also: [Hyperparameter optimization](/source/Hyperparameter_optimization)

Hyperparameter Optimization using Grid Search

Hyperparameter optimization involves finding the optimal value of non-trainable parameters, defined by the user. Examples of hyperparameters are [learning rate](/source/Learning_rate), number of layers or neurons, regularization strength, and tree depth. However, they strongly depend on the specific algorithm (e.g., [classification](/source/Classification), [regression](/source/Regression_analysis), [clustering](/source/Cluster_analysis), etc.).[6]

Hyperparameter optimization can be especially relevant when dealing with large-scale problems or limited resources, as it improves model accuracy, reduces overfitting, and decreases training time.[1] However, when the hyperparameter space increases, it may become computationally expensive. Hence, there are methods (e.g., [grid search](/source/Grid_search), [random search](/source/Random_search), or [bayesian optimization](/source/Bayesian_optimization)) that considerably simplify this process.[1]

## Features

Optuna is designed to optimize the model hyperparameters by searching large spaces and discarding combinations that show no significant improvements in the model. Moreover, it can parallelize the hyperparameter search over multiple threads or processes. Optuna works with a high degree of modularity, allowing the definition of different configurations for searching hyperparameters. It is possible to choose which hyperparameters to optimize and how to optimize them. Additionally, it permits parameter customization at runtime, meaning that the values to explore for the hyperparameters (i.e., the search space) can be defined while writing the code, rather than being defined in advance.[1]

Hyperparameter Optimization using Tree-Structured Parzen Estimators

### Sampling

Optuna exploits well-established algorithms to perform hyperparameter optimization, progressively reducing the search space in light of objective values. Examples are gaussian-process-based algorithms (i.e., a gaussian process to model the objective function[7]), [tree-structured parzen estimator](/source/Tree-structured_Parzen_estimators) (TPE) (i.e., a model-based optimization method that estimates the objective function and selects the best hyperparameters[8]), and [random search](/source/Random_search) (i.e., a basic optimization approach used for benchmarking[9]).

### Early stopping

Bayesian optimization of a function (black) with a gaussian process (purple). Three acquisition functions (blue).

Optuna includes a pruning feature to stop trials early when the results show no significant improvement in the model. This allows for the prevention of unnecessary computation, and it is used for models with long training times in order to save time and computational resources. Specifically, Optuna exploits techniques such as median and threshold [pruning](/source/Pruning).[10][11]

### Scalability

Optuna is designed to scale with distributed computing resources, supporting [parallel execution](/source/Parallel_execution_units). This feature allows users to run optimization trials across multiple processors or machines.[12]

### Integration with third-part libraries

Optuna integrates with various ML libraries and frameworks:[1]

- [Catboost](/source/CatBoost)

- [Dask](/source/Dask_(software))

- [Fast.ai](/source/Fast.ai)

- [Keras](/source/Keras)

- [LightGBM](/source/LightGBM)

- MLflow

- [PyTorch](/source/PyTorch)

- PyTorch Ignite

- [PyTorch Lightning](/source/PyTorch_Lightning)

- [TensorBoard](/source/TensorBoard)

- [TensorFlow](/source/TensorFlow)

- tf.keras

- Weights & Biases

- [XGBoost](/source/XGBoost)

- skrub

Moreover, Optuna offers a real-time dashboard that allows a user to monitor, through graphs and tables, the optimization history and the hyperparameter importance.[13]

## Applications

Optuna was designed to be framework agnostic so that it can be used with any machine-learning (ML) or deep-learning (DL) framework.[1]

Standard pipeline for the optimization of hyperparameters for the training of a Decision Tree Classfier using Optuna

### Machine learning

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Optuna can be used to optimize hyperparamenters of ML models. Examples are:

- [Random forest](/source/Random_forest): number of trees, maximum depth, and minimum samples per leaf.[14]

- [Gradient boosting machines](/source/Gradient_Boosting_Machines) (GBM): learning rate, number of estimators, and maximum depth.[15]

- [Support vector machines](/source/Support_vector_machine) (SVM): regularization parameter (C), kernel type (e.g., linear, radial basis function), and gamma (gamma).[16]

- [K-nearest neighbors](/source/K-nearest_neighbors_algorithm) (KNN): number of neighbors (k), distance metrics (e.g., Euclidean or Manhattan), and weight function.[17]

- [Linear](/source/Linear_regression) and [logistic](/source/Logistic_regression) regression: alpha in Ridge Regression or C in Logistic Regression.[18]

- [Naive Bayes](/source/Naive_bayes_classifier): smoothing coefficients.[19]

### Deep learning

In the context of deep learning, Optuna can be exploited during the training of [neural networks](/source/Neural_network) (NN) to optimize learning rate, batch size, and the number of hidden layers. For example, it can be used for:

- [Convolutional neural networks](/source/Convolutional_neural_network) (CNNs), for image classification, object detection, and semantic-segmentation tasks.[20]

- [Recurrent neural networks](/source/Recurrent_neural_network) (RNNs), for sequence-based tasks such as time-series forecasting and natural language processing.[21]

- [Transformers](/source/Transformers), for NLP tasks such as text classification, sentiment analysis, and machine translation.[22]

## Domains

Optuna has found applications in various research studies and industry implementations across different applicative domains.[2][3][4][5]

### Healthcare

In [healthcare](/source/Health_care), Optuna is currently utilized for [medical image analysis](/source/Medical_image_analysis) to optimize DL models for tumor detection,[23][24] disease identification,[25] multi-organ semantic segmentation,[26] and radiological image analysis. It can be also implemented for [disease prediction](/source/Disease_prediction), in particular for the improvement in the accuracy of predictive models for disease diagnosis and treatment outcomes.[27] Its application can be also found in [genomics](/source/Genomics) to predict genetic diseases and to identify genetic variations.[28]

### Finance

In finance, Optuna is used to optimize models for [algorithmic trading](/source/Algorithmic_trading). It allows for predicting market movements due to its ability to handle wide parameter ranges and complex model structures.[29][30] In particular, it is exploited for financial risk analysis and forecasting. Topics addressed are credit, market, and operational risks.[30]

### Autonomous systems

Optuna is used for real-time applications in autonomous systems for [robotics](/source/Robotics),[5] supporting decision making in dynamic environments. It is also used in the context of [self-driving cars](/source/Self-driving_car) to optimize the model to navigate safely in complex environments. For example, Optuna can be used in scenarios where there is the need to evaluate the rate and the severity of accidents[31] or to address the issue of network intrusion attacks due to potential vulnerabilities.[32][33]

### Natural language processing (NLP)

Optuna has also been applied in NLP. For example, it has been used for [text classification](/source/Text_Classification) to classify a piece of written text into categories (e.g., spam vs. not spam, topic modeling).[34] Another task is [sentiment analysis](/source/Sentiment_analysis), namely the detection of feelings expressed in text, particularly exploited for the analysis of content from social media and customer reviews.[35]

### Reinforcement learning (RL)

For what concerns RL, Optuna is utilized in the [gaming](/source/Gaming_computer) field to improve the model performance in games and virtual environments. In [robotics](/source/Robotics) it has been used to optimize decision-making processes in robotic systems for tasks like manipulation and navigation. Additionally, in the field of [autonomous vehicles](/source/Autonomous_vehicles) Optuna can optimize RL models to obtain enhanced safety, and a more efficient navigation strategy.[36]

## See also

- [Hyperparameter tuning](/source/Hyperparameter_tuning)

- [Bayesian optimization](/source/Bayesian_optimization)

- [Machine learning](/source/Machine_learning)

- [Deep learning](/source/Deep_learning)

## References

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1. **[^](#cite_ref-24)** Kumar Sahu, Prabhat; Fatma, Taiyaba (2025-02-07). ["Optimized Breast Cancer Classification Using PCA-LASSO Feature Selection and Ensemble Learning Strategies With Optuna Optimization"](https://doi.org/10.1109%2FACCESS.2025.3539746). *IEEE Access*. **13**: 35645–35661. [Bibcode](/source/Bibcode_(identifier)):[2025IEEEA..1335645K](https://ui.adsabs.harvard.edu/abs/2025IEEEA..1335645K). [doi](/source/Doi_(identifier)):[10.1109/ACCESS.2025.3539746](https://doi.org/10.1109%2FACCESS.2025.3539746). [ISSN](/source/ISSN_(identifier)) [2169-3536](https://search.worldcat.org/issn/2169-3536).

1. **[^](#cite_ref-25)** Deepan, P.; Prabhakar Reddy, G.; Arsha Reddy, M.; Vidya, R.; Dhiravidaselvi, S. (2024), Bhattacharya, Pronaya; Liu, Haipeng; Dutta, Pushan Kumar; Rodrigues, Joel J. P. C. (eds.), "Maximizing Accuracy in Alzheimer's Disease Prediction: A Optuna Hyper Parameter Optimization Strategy Using MRI Images", *Revolutionizing Healthcare 5.0: The Power of Generative AI: Advancements in Patient Care Through Generative AI Algorithms*, Cham: Springer Nature Switzerland, pp. 77–91, [doi](/source/Doi_(identifier)):[10.1007/978-3-031-75771-6_5](https://doi.org/10.1007%2F978-3-031-75771-6_5), [ISBN](/source/ISBN_(identifier)) [978-3-031-75771-6](https://en.wikipedia.org/wiki/Special:BookSources/978-3-031-75771-6){{[citation](https://en.wikipedia.org/wiki/Template:Citation)}}: CS1 maint: work parameter with ISBN ([link](https://en.wikipedia.org/wiki/Category:CS1_maint:_work_parameter_with_ISBN))

1. **[^](#cite_ref-26)** Vaiyapuri, Thavavel (2025-08-01). ["An Optuna-Based Metaheuristic Optimization Framework for Biomedical Image Analysis"](https://etasr.com/index.php/ETASR/article/view/11234). *Engineering, Technology & Applied Science Research*. **15** (4): 24382–24389. [doi](/source/Doi_(identifier)):[10.48084/etasr.11234](https://doi.org/10.48084%2Fetasr.11234). [ISSN](/source/ISSN_(identifier)) [1792-8036](https://search.worldcat.org/issn/1792-8036).

1. **[^](#cite_ref-27)** Srinivas, Polipireddy; Katarya, Rahul (2022-03-01). ["hyOPTXg: OPTUNA hyper-parameter optimization framework for predicting cardiovascular disease using XGBoost"](https://www.sciencedirect.com/science/article/pii/S1746809421010533). *Biomedical Signal Processing and Control*. **73** 103456. [doi](/source/Doi_(identifier)):[10.1016/j.bspc.2021.103456](https://doi.org/10.1016%2Fj.bspc.2021.103456). [ISSN](/source/ISSN_(identifier)) [1746-8094](https://search.worldcat.org/issn/1746-8094).

1. **[^](#cite_ref-28)** Shen, Zijie; Shen, Enhui; Zhu, Qian-Hao; Fan, Longjiang; Zou, Quan; Ye, Chu-Yu (2023). ["GSCtool: A Novel Descriptor that Characterizes the Genome for Applying Machine Learning in Genomics"](https://doi.org/10.1002%2Faisy.202300426). *Advanced Intelligent Systems*. **5** (12) 2300426. [doi](/source/Doi_(identifier)):[10.1002/aisy.202300426](https://doi.org/10.1002%2Faisy.202300426). [ISSN](/source/ISSN_(identifier)) [2640-4567](https://search.worldcat.org/issn/2640-4567).

1. **[^](#cite_ref-29)** Shui, Hongyi; Sha, Xinye; Chen, Baizheng; Wu, Jiajie (2024-08-26). ["Stock weighted average price prediction based on feature engineering and Lightgbm model"](https://dl.acm.org/doi/10.1145/3677892.3677945). *Proceedings of the 2024 International Conference on Digital Society and Artificial Intelligence*. DSAI '24. New York, NY, USA: Association for Computing Machinery. pp. 336–340. [doi](/source/Doi_(identifier)):[10.1145/3677892.3677945](https://doi.org/10.1145%2F3677892.3677945). [ISBN](/source/ISBN_(identifier)) [979-8-4007-0983-8](https://en.wikipedia.org/wiki/Special:BookSources/979-8-4007-0983-8).

1. ^ [***a***](#cite_ref-:0_30-0) [***b***](#cite_ref-:0_30-1) Garg, Deepak; Shelke, Nitin Arvind; Kitukale, Gauri; Mehlawat, Nishka (2024-04-05). "Leveraging Financial Data and Risk Management in Banking sector using Machine Learning". *2024 IEEE 9th International Conference for Convergence in Technology (I2CT)*. pp. 1–6. [doi](/source/Doi_(identifier)):[10.1109/I2CT61223.2024.10544336](https://doi.org/10.1109%2FI2CT61223.2024.10544336). [ISBN](/source/ISBN_(identifier)) [979-8-3503-9445-0](https://en.wikipedia.org/wiki/Special:BookSources/979-8-3503-9445-0).

1. **[^](#cite_ref-31)** Rezashoar, Soheil; Kashi, Ehsan; Saeidi, Soheila (2024-07-26). "A hybrid algorithm based on machine learning (LightGBM-Optuna) for road accident severity classification (case study: United States from 2016 to 2020)". *Innovative Infrastructure Solutions*. **9** (8): 319. [Bibcode](/source/Bibcode_(identifier)):[2024InnIS...9..319R](https://ui.adsabs.harvard.edu/abs/2024InnIS...9..319R). [doi](/source/Doi_(identifier)):[10.1007/s41062-024-01626-y](https://doi.org/10.1007%2Fs41062-024-01626-y). [ISSN](/source/ISSN_(identifier)) [2364-4184](https://search.worldcat.org/issn/2364-4184).

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1. **[^](#cite_ref-33)** Parekh, Nishank; Sen, Arzob; Rajasekaran, P.; Jayaseeli, J. D. Dorathi; Robert, P. (2024-12-17). "Network Intrusion Detection System Using Optuna". *2024 International Conference on IoT Based Control Networks and Intelligent Systems (ICICNIS)*. pp. 312–318. [doi](/source/Doi_(identifier)):[10.1109/ICICNIS64247.2024.10823298](https://doi.org/10.1109%2FICICNIS64247.2024.10823298). [ISBN](/source/ISBN_(identifier)) [979-8-3315-1809-7](https://en.wikipedia.org/wiki/Special:BookSources/979-8-3315-1809-7).

1. **[^](#cite_ref-34)** Rahmi, Nadya Alinda; Defit, Sarjon; Okfalisa (2024-12-31). ["The Use of Hyperparameter Tuning in Model Classification: A Scientific Work Area Identification"](http://joiv.org/index.php/joiv/article/view/3092). *International Journal on Informatics Visualization*. **8** (4): 2181. [doi](/source/Doi_(identifier)):[10.62527/joiv.8.4.3092](https://doi.org/10.62527%2Fjoiv.8.4.3092). [ISSN](/source/ISSN_(identifier)) [2549-9904](https://search.worldcat.org/issn/2549-9904).

1. **[^](#cite_ref-35)** Efendi, Akmar; Fitri, Iskandar; Nurcahyo, Gunadi Widi (2024-08-07). "Improvement of Machine Learning Algorithms with Hyperparameter Tuning on Various Datasets". *2024 International Conference on Future Technologies for Smart Society (ICFTSS)*. pp. 75–79. [doi](/source/Doi_(identifier)):[10.1109/ICFTSS61109.2024.10691354](https://doi.org/10.1109%2FICFTSS61109.2024.10691354). [ISBN](/source/ISBN_(identifier)) [979-8-3503-7384-4](https://en.wikipedia.org/wiki/Special:BookSources/979-8-3503-7384-4).

1. **[^](#cite_ref-36)** Kiran, B Ravi; Sobh, Ibrahim; Talpaert, Victor; Mannion, Patrick; Sallab, Ahmad A. Al; Yogamani, Senthil; Pérez, Patrick (2021-02-09). "Deep Reinforcement Learning for Autonomous Driving: A Survey". *IEEE Transactions on Intelligent Transportation Systems*. **23** (6): 4909–4926. [arXiv](/source/ArXiv_(identifier)):[2002.00444](https://arxiv.org/abs/2002.00444). [Bibcode](/source/Bibcode_(identifier)):[2022ITITr..23.4909K](https://ui.adsabs.harvard.edu/abs/2022ITITr..23.4909K). [doi](/source/Doi_(identifier)):[10.1109/TITS.2021.3054625](https://doi.org/10.1109%2FTITS.2021.3054625). [ISSN](/source/ISSN_(identifier)) [1558-0016](https://search.worldcat.org/issn/1558-0016).

## Further reading

- Wang, Xilu; Jin, Yaochu; Schmitt, Sebastian; Olhofer, Markus (2023-07-13). ["Recent Advances in Bayesian Optimization"](https://dl.acm.org/doi/10.1145/3582078). *ACM Comput. Surv*. **55** (13s): 287:1–287:36. [doi](/source/Doi_(identifier)):[10.1145/3582078](https://doi.org/10.1145%2F3582078). [ISSN](/source/ISSN_(identifier)) [0360-0300](https://search.worldcat.org/issn/0360-0300).

- Shinde, Pramila P.; Shah, Seema (2018-08-16). "A Review of Machine Learning and Deep Learning Applications". *2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA)*. pp. 1–6. [doi](/source/Doi_(identifier)):[10.1109/ICCUBEA.2018.8697857](https://doi.org/10.1109%2FICCUBEA.2018.8697857). [ISBN](/source/ISBN_(identifier)) [978-1-5386-5257-2](https://en.wikipedia.org/wiki/Special:BookSources/978-1-5386-5257-2).

## External links

- [Official website](https://optuna.org/)

- [Optuna](https://github.com/Optuna) on [GitHub](/source/GitHub)

- [Optuna Documentation](https://optuna.readthedocs.io/en/stable/)

- [Tutorial](https://optuna.readthedocs.io/en/stable/tutorial/)

- [Optuna Dashboard](https://github.com/optuna/optuna-dashboard/)

- [Optuna Dashboard Documentation](https://optuna-dashboard.readthedocs.io/en/stable/getting-started.html/)

---
Adapted from the Wikipedia article [Optuna](https://en.wikipedia.org/wiki/Optuna) by Wikipedia contributors ([contributor history](https://en.wikipedia.org/wiki/Optuna?action=history)). Available under [Creative Commons Attribution-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-sa/4.0/). Changes may have been made.