Gradient Boosting Machine is a machine learning model for classification and regression problems. In the following, we present a Condensed Gradient Boosting model that works well for multiclass multi-output regression with high precision and speed.
To train the CGB model for both multiclass classification and multioutput regression, first, it should be installed using pip.
pip install .
After importing the class, define the model with hyperparameters or use the default values for it. Models run on both Windows and Linux.
To access more examples, plots, and related codes, please refer to C_GB-EX.
On the wiki page, the implementation of the algorithm for two problems (classification and regression) is described.
Cite this package as below.
@article{emami2022condensed,
title={Condensed Gradient Boosting},
author={Emami, Seyedsaman and Mart{\'\i}nez-Mu{\~n}oz, Gonzalo},
journal={arXiv preprint arXiv:2211.14599},
year={2022}
}
0.0.5
09.Jul.2023
01.Oct.2021
- wiki, the model introduction along with complete examples, API, hyperparameters. Refer wiki
- Examples, codes to reproduce the results, and additional experiments. Refer C_GB-EX.
- For the condensed model and analysis features, refer to our paper
- For instructions, please refer to the documentation.