XAI/Interpretable ML SDK for model exploration and explanation
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DALEX provides a model-agnostic framework for exploring and explaining the behavior of complex predictive models, targeting data scientists and ML engineers who need to understand and validate their black-box models. It aims to increase trust and adoption of ML by offering tools for local and global model explanations.
How It Works
DALEX wraps any predictive model, creating an "explainer" object. This object can then be used with various local and global explainers, such as SHAP or LIME, to analyze variable importance, partial dependence, and individual prediction explanations. This approach allows users to apply consistent explanation methods across diverse modeling libraries.
Quick Start & Requirements
install.packages("DALEX")
pip install dalex -U
or conda install -c conda-forge dalex
Highlighted Details
DALEXtra
extension.Maintenance & Community
The project is actively maintained, with significant contributions and academic backing. It is part of the DrWhy.AI universe. Links to community resources like Discord/Slack are not explicitly provided in the README.
Licensing & Compatibility
The R package is available on CRAN, and the Python package on PyPI and conda-forge. The project cites two JMLR papers, indicating academic rigor. Licensing details are not explicitly stated but are typically permissive for CRAN/PyPI packages, allowing commercial use.
Limitations & Caveats
While model-agnostic, the effectiveness and computational cost of explanations can vary significantly depending on the underlying model's complexity and the chosen explainer. The README emphasizes the "approximated" nature of some explainers.
6 days ago
Inactive