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| 1 | +# Azure Machine Learning Responsible AI Dashboard and Scorecard |
| 2 | + |
| 3 | +Read more about how to generate the Responsible AI (RAI) dashboard [here](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai-dashboard-sdk-cli?tabs=yaml) and Responsible AI scorecard [here](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai-scorecard). |
| 4 | + |
| 5 | +The Responsible AI components are supported for MLflow models with `scikit-learn` flavor that are trained on `pandas.DataFrame`. |
| 6 | +The components accept both models and SciKit-Learn pipelines as input as long as the model or pipeline implements `predict` and `predict_proba` functions that conforms to the `scikit-learn` convention. |
| 7 | +If not compatible, you can wrap your model's prediction function into a wrapper class that transforms the output into the format that is supported (`predict` and `predict_proba` of `scikit-learn`), and pass that wrapper class to modules in this repo. |
| 8 | + |
| 9 | +## Sample directory 📖 |
| 10 | + |
| 11 | +| Scenario | Dataset | Data type | RAI component included | Link to sample | |
| 12 | +| --- | --- | --- | --- | --- | |
| 13 | +| Regression | [sklearn Diabetes](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_diabetes.html) | Tabular | Explanation, Error Analysis, Causal analysis, Counterfactuals | ADD LINK AFTER MOVING SAMPLES! | |
| 14 | +| Regression | ADD LINK AFTER MOVING PROGRAMMERS DATA FOLDER | Tabular | Explanation, Error Analysis, Causal analysis, Counterfactuals | ADD LINK AFTER MOVING SAMPLES! | |
| 15 | +| Classification | [Kaggle Housing](https://www.kaggle.com/alphaepsilon/housing-prices-dataset) | Tabular | Explanation, Error Analysis, Causal analysis, Counterfactuals | ADD LINK AFTER MOVING SAMPLES! | |
| 16 | +| Decision making | [Kaggle Housing](https://www.kaggle.com/alphaepsilon/housing-prices-dataset) | Tabular | Causal analysis, Counterfactuals | ADD LINK AFTER MOVING SAMPLES! | |
| 17 | +| Decision making | [sklearn Diabetes](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_diabetes.html) | Tabular | Causal analysis, Counterfactuals | ADD LINK AFTER MOVING SAMPLES! | |
| 18 | + |
| 19 | + |
| 20 | +## Supportability 🧰 |
| 21 | +Currently, we support datasets having numerical and categorical features. The following table provides the scenarios supported for each of the four responsible AI components: |
| 22 | +> **Note**: Model overview (performance metrics and fairness disparity metrics) and Data explorer are generated for every Responsible AI dashboard by default and do not require a component to be configured. |
| 23 | +
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| 24 | +| RAI component | Binary classification | Multi-class classification | Multilabel classification | Regression | Timeseries forecasting | Categorical features | Text features | Image Features | Recommender Systems | Reinforcement Learning | |
| 25 | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | -- | |
| 26 | +| Explainability | Yes | Yes | No | Yes | No | Yes | No | No | No | No | |
| 27 | +| Error Analysis | Yes | Yes | No | Yes | No | Yes | No | No | No | No | |
| 28 | +| Causal Analysis | Yes | No | No | Yes | No | Yes (max 5 features due to computational cost) | No | No | No | No | |
| 29 | +| Counterfactual | Yes | Yes | No | Yes | No | Yes | No | No | No | No | |
| 30 | + |
| 31 | +Read more about how to use the Responsible AI dashboard [here](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai-dashboard). |
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