Metadata-Version: 2.0
Name: azureml-explain-model
Version: 1.0.21
Summary: UNKNOWN
Home-page: https://docs.microsoft.com/en-us/azure/machine-learning/service/
Author: Microsoft Corp
License: https://aka.ms/azureml-sdk-license
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: Other/Proprietary 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/x-rst
Provides-Extra: mimic
Provides-Extra: deep
Provides-Extra: sample
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: scipy
Requires-Dist: scikit-learn
Requires-Dist: shap (<=0.28.5,>=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 Azure Machine Learning Explain Model API for Python
===================================================

This package has been tested with Python 2.7 and 3.6.
=========================

The SDK is released with backwards compatibility guarantees.

Machine learning (ML) explain model package is used to explain 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




