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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 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
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