The model_tuner
library 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 library is particularly useful for model selection, hyperparameter tuning, and ensuring optimal performance across different metrics.
Before installing model_tuner
, ensure your system meets the following requirements:
model_tuner
requires Python 3.7 or higher. Specific dependency versions vary depending on your Python version.
The following dependencies will be automatically installed when you install model_tuner
via pip:
joblib==1.3.2
tqdm==4.66.4
catboost==1.2.7
pip==24.0
numpy==1.21.4
pandas==1.1.5
scikit-learn==0.23.2
scipy==1.4.1
imbalanced-learn==0.7.0
scikit-optimize==0.8.1
xgboost==1.6.2
joblib==1.3.2
tqdm==4.66.4
catboost==1.2.7
pip==24.2
setuptools==75.1.0
wheel==0.44.0
numpy>=1.19.5, <2.0.0
pandas>=1.3.5, <2.2.3
scikit-learn>=1.0.2, <1.4.0
scipy>=1.6.3, <1.11
imbalanced-learn==0.12.4
scikit-optimize==0.10.2
xgboost==2.1.2
joblib==1.3.2
tqdm==4.66.4
catboost==1.2.7
pip==24.2
setuptools==75.1.0
wheel==0.44.0
numpy>=1.19.5, <2.0.0
pandas>=1.3.5, <2.2.2
scikit-learn==1.5.1
scipy==1.14.0
imbalanced-learn==0.12.4
scikit-optimize==0.10.2
xgboost==2.1.2
You can install model_tuner
directly from PyPI:
pip install model_tuner
https://uclamii.github.io/model_tuner
model_tuner
is distributed under the Apache License. See LICENSE for more information.
model_tuner
If you use model_tuner
in your research or projects, please consider citing it.
@software{funnell_2024_12727322,
author = {Funnell, Arthur and
Shpaner, Leonid and
Petousis, Panayiotis},
title = {Model Tuner},
month = jul,
year = 2024,
publisher = {Zenodo},
version = {0.0.23a},
doi = {10.5281/zenodo.12727322},
url = {https://doi.org/10.5281/zenodo.12727322}
}
If you have any questions or issues with model_tuner
, please open an issue on this GitHub repository.
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.