DALEX: moDel Agnostic Language for Exploration and eXplanation

dalex: Responsible Machine Learning in Python

Unverified black box model is the path to the failure. Opaqueness leads to distrust. Distrust leads to ignoration. Ignoration leads to rejection.

The dalex package xrays any model and helps to explore and explain its behaviour, helps to understand how complex models are working. The main Explainer object creates a wrapper around a predictive model. Wrapped models may then be explored and compared with a collection of model-level and predict-level explanations. Moreover, there are fairness methods and interactive exploration dashboards available to the user.

The philosophy behind dalex explanations is described in the Explanatory Model Analysis book.

The dalex package is available on PyPI and conda-forge.

pip install dalex -U

conda install -c conda-forge dalex

One can install optional dependencies for all additional features using pip install dalex[full].

Introduction to the dalex package: Titanic: tutorial and examples
Key features explained: FIFA20: explain default vs tuned model with dalex
How to use dalex with: xgboost, tensorflow, h2o (feat. autokeras, catboost, lightgbm)
More explanations: residuals, shap, lime
Introduction to the Fairness module in dalex
Tutorial on bias detection with dalex
Introduction to the Aspect module in dalex
Introduction to the Arena module in dalex
Arena documentation: Getting Started & Demos
Code in the form of jupyter notebook

This package uses plotly to render the plots:

Install extensions to use plotly in JupyterLab: Getting Started Troubleshooting
Use show=False parameter in plot method to return plotly Figure object
It is possible to edit the figures and save them

If you use dalex, please cite our JMLR paper:

@article{JMLR:v22:20-1473,
  author  = {Hubert Baniecki and
             Wojciech Kretowicz and
             Piotr Piatyszek and 
             Jakub Wisniewski and 
             Przemyslaw Biecek},
  title   = {dalex: Responsible Machine Learning 
             with Interactive Explainability and Fairness in Python},
  journal = {Journal of Machine Learning Research},
  year    = {2021},
  volume  = {22},
  number  = {214},
  pages   = {1-7},
  url     = {http://jmlr.org/papers/v22/20-1473.html}
}

There is a detailed instruction on how to add native support for a new model/framework into dalex, and how to add a new explanation method.