ExplainX.ai is a fast, scalable & state-of-the-art explainable AI platform. ExplainX.ai helps data scientists understand, explain, debug and validate any machine learning model - in just one line of code.

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

• Installing explainX
• Working Examples
• explainX Dashboard Features
• Documentation
• Provide Feedback to Improve explainX.ai

• Desktop: You can use explainX on your own computer in under a minute. If you already have a python environment setup, just run the following command.

pip install explainx

• Jupyter Notebook: You can also install explainx via Jupyter Notebook. Just run the following command:

!pip install explainx

Once you have install explainX, you can simply follow the example below to use it:

Import explainx

from explainx import *

Load dataset as X_Data, Y_Data in your XGBoost Model

#X_Data = Pandas DataFrame #Y_Data = Numpy Array or List X_Data, Y_Data = explainx.dataset_boston() #Train Model model = xgboost.train({"learning_rate": 0.01}, xgboost.DMatrix(X_Data, label=Y_Data), 100)

One line of code to use the explainx module

explainx.ai(X_Data, Y_Data, model, model_name="xgboost")

Click on the link to view the dashboard:

App running on https://127.0.0.1:8050 

Learn to analyze the dashboard by following this link: explainX Dashboard Features

Visit the documentation to learn more

CatBoost, XGBoost, Scikit-learn Models, SVM, Neural Networks

Please click on the image below to load the tutorial.

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

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

MIT