On the recordRead 14 Nov 202614 facts
AIX360
Plans and prices
AIX360
Free
No price or usage limits stated
Read 14 Nov 2026 · github.com
On the record
- Algorithm examples
- Listed methods include LIME, SHAP, ProtoDash, and time-series explanation approaches.github.com · read 14 Nov 2026
- Algorithms and metrics
- Its Python package includes algorithms covering different explanation dimensions and proxy explainability metrics.github.com · read 14 Nov 2026
- Audience and support
- The toolkit provides data-scientist-oriented tutorials and directs contributors to a Slack community.github.com · read 14 Nov 2026
- Data types
- The toolkit supports tabular, text, image, and time-series data.github.com · read 14 Nov 2026
- Dependencies
- Optional algorithm-specific dependencies can require conflicting Python versions, so not every algorithm combination can share one environment.github.com · read 14 Nov 2026
- Extensibility
- The library was developed with extensibility in mind and invites contributions of algorithms, metrics, and use cases.github.com · read 14 Nov 2026
- Guidance
- The project provides guidance material and a taxonomy tree to help select explanation algorithms.github.com · read 14 Nov 2026
- Install
- AIX360 can be installed from PyPI using pip install aix360.github.com · read 14 Nov 2026
- Project status
- The project describes the library as still in development.github.com · read 14 Nov 2026
- Purpose
- AIX360 is an open-source Python toolkit for interpreting and explaining datasets and machine-learning models.github.com · read 14 Nov 2026
- Python versions
- Python requirements vary by algorithm; the installation table lists versions including 3.6 and 3.10.github.com · read 14 Nov 2026
- Security and license
- The repository links to a security policy and identifies an Apache-2.0 license.github.com · read 14 Nov 2026
- Tutorials
- The repository includes example and tutorial Jupyter notebooks.github.com · read 14 Nov 2026
