ExplainX is a fast, light-weight and scalable Explainable AI framework for data scientists. It enables you to explain and debug state of the art machine learning models in as simple as one line of code.

Why we need explainability & interpretibility?

Essential for:

1. Model debugging - Why did my model make a mistake? How can I improve the accuracy of the model?
2. Detecting fairness issues - Is my model biased? If yes, where?
3. Human-AI cooperation - How can I understand and trust the model's decisions?
4. Regulatory compliance - Does my model satisfy legal & regulatory requirements?
5. High-risk applications - Healthcare, Financial Services, FinTech, Judicial, Security etc,.

Visit explainx.ai website to learn more: https://www.explainx.ai

Installation on your laptop.

• You can use explainX on your own computer in under a minute.

• Make sure you have Python 3.5+

• Open the terminal and run the following to create a new folder explainx.

mkdir explainx

cd explainx

• Run the following to create a new virtual environment.

python3 -m venv env_explainx

• Then run the following command to activate the virtual environment.

source env_explainx/bin/activate

• Download explainx_pro code using the following.

git clone https://github.com/explainX/explainx_pro.git

• Run the following to install all required Python Libraries.

pip install -r explainx_pro/requirements.txt

Run explainx_pro

• Download the tutorial file found here and place it in explainx folder

• Run the following in the terminal to run Jupyter.

jupyter notebook

• Open tutorial_explainx_decision_rule.ipynb and run all cells.

Contributing

Pull requests are welcome. In order to make changes to explainx, the ideal approach is to fork the repository then clone the fork locally.

For major changes, please open an issue first to discuss what you would like to change. Please make sure to update tests as appropriate.

Report Issues

Please help us by reporting any issues you may have while using explainx_pro.