XAISuite: Training and Explanation Generation Utilities for Machine Learning Models
Reason this release was yanked:
This is an unstable version without complete functionalities.
Project description
XAISuite: Training and Explaining Machine Learning Models
Table of Contents
Introduction
XAISuite (Explanatory Artificial Intelligence Suite is a library for training and explaining machine learning models for tabular datasets in Python. It provides a unified interface for training any sklearn model using just a line of code and allows users to easily comparing the results of different explainers!
XAISuite accomplishes machine learning model training and explanation generation in three steps: (1) data loading, (2) model training and (3) explanation generation. Each of these steps are delved into more detail in our documentation and in the demo tutorials.
XAISuite was created as a helper library to [this paper](insert link), which studied the difference in SHAP and LIME explanations for different models on tabular datasets.
Installation
You can install the XAI Suite
through PyPI:
pip install XAISuite
Getting Started
For example code and an introduction to the library, see the Demo Folder.
If you are looking for a model or dataset to use, sklearn has several cool options.
How to Contribute
We welcome the contribution from the open-source community to improve the library!
To add a new functionality into the library, please follow the template and steps demonstrated in this documentation. Note that, for the time being, XAISuite only supports tabular datasets and image datasets in tabular form.
Technical Report and Citing XAISuite
A paper proposing and using XAISuite to compare explanatory methods is still in pre-publication. Use the following BibTex to cite XAISuite:
@article{mitra2022-xaisuite,
author = {Shreyan Mitra and Leilani Gilpin},
title = {Comparison of SHAP and LIME Explanations for Supervised
Machine Learning Models Trained on Tabular Datasets},
year = {2022},
doi = {},
url = {},
archivePrefix = {},
eprint = {},
}
Contact Us
If you have any questions, comments or suggestions, please do not hesitate to contact us at shreyan.m.mitra@gmail.com
License
This work is licensed under a BSD 3-Clause License.
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