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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
===================================================
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
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