Factual package intelligence from PyPI

model-explain 0.5.0 package intelligence

A comprehensive Python package for interpreting and explaining machine learning and deep learning models. Includes support for feature attention mechanisms and integrates popular explanation methods such as LIME, SHAP, Grad-CAM, permutation importance, and saliency maps. Offers a

pip install model-explain

Current version
0.5.0
Python requirement
>=3.8
License
Not declared
Distribution type
Pure Python wheel
Release files
2 (1 wheels, 1 source)
Download size
62.1 KiB
Release history
7 releases with files
Median release cadence
3 days

model-explain dependencies

PyPI declares 14 unique dependency rules for this release. Environment markers are shown when supplied by the project.

  1. torch <3.0.0,>=2.0.0
  2. plotly <6.0.0,>=5.0.0
  3. fairlearn <0.11.0,>=0.8.0
  4. dice-ml <1.0,>=0.9
  5. numpy >=2.2.6
  6. pandas <2.0.0,>=1.3.0
  7. scikit-learn <2.0.0,>=1.0.0
  8. shap <1.0.0,>=0.41.0
  9. lime <0.3.0,>=0.2.0.1
  10. streamlit <2.0.0,>=1.0.0
  11. xgboost <2.0.0,>=1.5.0
  12. joblib <2.0.0,>=1.0.0
  13. matplotlib <4.0.0,>=3.4.0
  14. seaborn <1.0.0,>=0.11.0

Compatibility and release files

model-explain publishes 1 wheel and 1 source archive for version 0.5.0. Wheel platform tags: any.

No version-specific Python classifiers are declared.

Metadata completeness

PyPI does not currently declare: license. PyDeps marks these fields as unknown instead of guessing values.

Release activity and sources

PyPI lists 7 releases with files. The first dated release is ; 7 releases fall within the 365 days preceding the latest dated release. The current release files were uploaded on . The preceding dated release was .