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Oct 25, 2025
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Update Readme #10

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Built for machine learning practitioners requiring flexible and robust hyperparameter tuning, **ConfOpt** delivers superior optimization performance through conformal uncertainty quantification and a wide selection of surrogate models.

**ConfOpt** also lends itself well to HPO research and as an add-on, requiring limited [dependancies](https://github.com/rick12000/confopt/blob/main/requirements.txt) and focusing on pure search methodology.

## 📦 Installation

Install ConfOpt from PyPI using pip:
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For detailed examples and explanations see the [documentation](https://confopt.readthedocs.io/).

## 🔗 Integrations

Advanced users should note **ConfOpt** doesn't currently support parallelization, multi-fidelity optimization and multi-objective optimization.

If you wish to use **ConfOpt** with parallelization or multi-fidelity/pruning, fear not, there's an [Optuna](https://github.com/optuna) integration that supports both. Parallelization support has been well tested, while multi-fidelity/pruning is still experimental (it should work well and has been spot tested, but if there are any problems please raise an issue).

For instructions on how to use **ConfOpt** in Optuna refer to the official documentation [here](https://hub.optuna.org/samplers/confopt_sampler/).

## 📚 Documentation

### **User Guide**
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