model-tuner 0.0.12a0

A Python library for tuning Machine Learning models.

The model_tuner class is a versatile and powerful tool designed to facilitate the training, evaluation, and tuning of machine learning models. It supports various functionalities such as handling imbalanced data, applying different scaling and imputation techniques, calibrating models, and conducting cross-validation. This class is particularly useful for model selection and hyperparameter tuning, ensuring optimal performance across different metrics.

Prerequisites

Before you install model_tuner, ensure your system meets the following requirements:

• Python: Version 3.7 or higher is required to run model_tuner.

Additionally, model_tuner depends on the following packages, which will be automatically installed when you install model_tuner using pip:

• numpy: Version 1.21.6 or higher

• pandas: Version 1.3.5 or higher

• joblib: Version 1.3.2 or higher

• scikit-learn: Version 1.0.2 or higher

• scipy: Version 1.7.3 or higher

• tqdm: Version 4.66.4 or higher

Installation

You can install model_tuner directly from PyPI:

pip install model_tuner

📄 Official Documentation

https://uclamii.github.io/model_tuner

🌐 Author Website

https://www.mii.ucla.edu/

⚖️ License

model_tuner is distributed under the Apache License. See LICENSE for more information.

📚 Citing model_tuner

If you use model_tuner in your research or projects, please consider citing it.

@software{arthur_funnell_2024_12727322,
  author       = {Arthur Funnell,
                  Leonid Shpaner and
                  Panayiotis Petousis},
  title        = {uclamii/model\_tuner: model tuner 0.0.12a},
  month        = jul,
  year         = 2024,
  publisher    = {Zenodo},
  version      = {0.0.12a},
  doi          = {10.5281/zenodo.12727322},
  url          = {https://doi.org/10.5281/zenodo.12727322}
}

Support

If you have any questions or issues with model_tuner, please open an issue on this GitHub repository.

Acknowledgements

This work was supported by the UCLA Medical Informatics Institute (MII) and the Clinical and Translational Science Institute (CTSI). Special thanks to Dr. Alex Bui for his invaluable guidance and support, and to Panayiotis Petousis for his original contributions to this codebase.