Metadata-Version: 2.1
Name: interpret-community
Version: 0.1.0.2
Summary: Microsoft Interpret Extensions SDK for Python
Home-page: https://docs.microsoft.com/en-us/azure/machine-learning/service/
Author: Microsoft Corp
Author-email: ilmat@microsoft.com
License: MIT License
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: MacOS
Classifier: Operating System :: POSIX :: Linux
Description-Content-Type: text/markdown
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: scipy
Requires-Dist: scikit-learn
Requires-Dist: packaging
Requires-Dist: interpret (>=0.1.17)
Requires-Dist: shap (<=0.29.3,>=0.20.0)
Provides-Extra: deep
Requires-Dist: tensorflow ; extra == 'deep'
Provides-Extra: mimic
Requires-Dist: lightgbm ; extra == 'mimic'
Provides-Extra: sample
Requires-Dist: hdbscan ; extra == 'sample'

# Microsoft Interpret Extensions SDK for Python

### This package has been tested with Python 2.7 and 3.6

This is the initial extensions SDK release.

Machine learning interpret community package is used to interpret black box ML models.

- The TabularExplainer can be used to give local and global feature importances
- The best explainer is automatically chosen for the user based on the model
- Local feature importances are for each evaluation row
- Global feature importances summarize the most importance features at the model-level
- The API supports both dense (numpy or pandas) and sparse (scipy) datasets
- For more advanced users, individual explainers can be used
- The KernelExplainer and MimicExplainer are for BlackBox models
- The MimicExplainer is faster but less accurate than the KernelExplainer
- The TreeExplainer is for tree-based models
- The DeepExplainer is for DNN tensorflow or pytorch models


