wehub-resource-sync
32 lines
1.3 KiB
Markdown
32 lines
1.3 KiB
Markdown
# SHAP Examples
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Examples demonstrating use of the `mlflow.shap` APIs for model explainability.
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| File | Task | Description |
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| :----------------------------------------------------------- | :------------------------ | :------------------------------------------------------------- |
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| [regression.py](regression.py) | Regression | Log explanations for a LinearRegression model |
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| [binary_classification.py](binary_classification.py) | Binary classification | Log explanations for a binary RandomForestClassifier model |
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| [multiclass_classification.py](multiclass_classification.py) | Multiclass classification | Log explanations for a multiclass RandomForestClassifier model |
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## Prerequisites
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Run the following command to install required packages:
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```
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pip install mlflow scikit-learn shap matplotlib
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```
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## How to run the scripts
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```bash
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python <script_name>
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```
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## How to view the logged explanations:
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- Run `mlflow server` to launch the MLflow UI.
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- Open http://127.0.0.1:5000 on your browser.
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- Click the latest run in the runs table.
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- Scroll down to the artifact viewer.
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- Open a folder named `model_explanations_shap`.
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