Machine Learning explain model package is used to interpret ML models
Project description
Microsoft Azure Machine Learning Explain Model API for Python
This package has been tested with Python 3.6 and 3.7.
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
Setup
Follow these instructions to install the Azure ML SDK on your local machine, create an Azure ML workspace, and set up your notebook environment, which is required for the next step. Once you have set up your environment, install the AzureML Explain Model package:
pip install azureml-explain-modelRelease history Release notifications | RSS feed
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