Build Explainable AI Dashboards

Table of Contents

1. Introduction
2. Explainer Dashboard Python Library
3. Installing the Explainer Dashboard
4. Features of the Explainer Dashboard
   1. SHAP Plots
   2. Permutation Importance
   3. Partial Dependence Plot
   4. SHAP Interaction Values
   5. Model Performance Plots
5. Example Code for Building the Dashboard
6. Documentation for the Explainer Dashboard
7. Heroku App for the Explainer Dashboard
8. Classification Explainer Dashboard
9. Regression Explainer Dashboard
10. Multi-Class Explainer Dashboard
11. Customizing Your Own Dashboard
12. Conclusion

Explaining the Explainer Dashboard Python Library

In this article, we will be discussing the Explainer Dashboard Python library and how it can be used to quickly build an explainable AI dashboard in Python. The Explainer Dashboard is a powerful tool that allows You to deploy a dashboard web application for your machine learning models. It is Based on the Explainer Dashboard Python library, which provides a range of features for building interactive dashboards.

Installing the Explainer Dashboard

Before we dive into the features of the Explainer Dashboard, let's first discuss how to install it. You can install the Explainer Dashboard using pip or conda. Once you have installed it, you can start building your dashboard web application.

Features of the Explainer Dashboard

The Explainer Dashboard provides a range of features for building interactive dashboards. Some of the key features include:

SHAP Plots

SHAP is a popular library in Python that provides a glimpse of the underlying mechanism on how features contribute to the model's prediction performance. With the Explainer Dashboard, you can get a glimpse of the contribution of the features toward the prediction.

Permutation Importance

The Explainer Dashboard also allows you to Create permutation importance plots. This means that if you shuffle a feature, the model performance deteriorates. You can use this to judge whether your model is robust or arises from chance prediction.

Partial Dependence Plot

The partial dependence plot allows you to evaluate the contribution of the individual features on the model's performance. This is a powerful tool for understanding how your model is making predictions.

SHAP Interaction Values

The SHAP interaction values plot essentially decomposes the SHAP value into the direct effect and interaction has. This allows you to observe the contribution of features.

Model Performance Plots

For classification models, you can display the model performance via precision plots, confusion matrix, ROC AUC plot, PR AUC plot, etc. For regression models, you can Show the goodness of fit plots, the residual plots, etc.

Example Code for Building the Dashboard

The Explainer Dashboard Python library provides example code that you can use to build your dashboard app. This code is available for both classification and regression models.

Documentation for the Explainer Dashboard

The documentation for the Explainer Dashboard is another great resource that you can use to learn how to implement the various functions provided by the Explainer Dashboard. The documentation provides some code that you can just copy and paste into your script files and run.

Heroku App for the Explainer Dashboard

The Explainer Dashboard also has a Heroku app that provides a well-laid-out high-level overview of some of the possible dashboards that you can create. This includes the classifier dashboard, the regression dashboard, the multi-class dashboard, and also the custom dashboard.

Classification Explainer Dashboard

The classification explainer dashboard provides a range of features for understanding how your classification model is making predictions. You can see the feature importance tab, the classification stats tab, individual predictions tab, what-if feature dependence, feature interactions, and also the decision tree.

Regression Explainer Dashboard

The regression explainer dashboard provides a range of features for understanding how your regression model is making predictions. You can see the feature importance tab, the goodness of fit plots, the residual plots, individual predictions tab, what-if feature dependence, feature interactions, and also the decision tree.

Multi-Class Explainer Dashboard

The multi-class explainer dashboard provides a range of features for understanding how your multi-class model is making predictions. You can see the feature importance tab, the classification stats tab, individual predictions tab, what-if feature dependence, feature interactions, and also the decision tree.

Customizing Your Own Dashboard

The custom dashboard is a bit more sophisticated because you can customize the layout and the look of the dashboard web application. It also provides you with information on how you could just install the library.

Conclusion

In conclusion, the Explainer Dashboard Python library is a powerful tool for building interactive dashboards for your machine learning models. It provides a range of features for understanding how your model is making predictions and allows you to customize the layout and the look of the dashboard web application. If you are interested in building an explainable AI dashboard in Python, then the Explainer Dashboard is definitely worth checking out.

Highlights

• The Explainer Dashboard Python library allows you to quickly build an explainable AI dashboard in Python.
• The library provides a range of features for building interactive dashboards, including SHAP plots, permutation importance, partial dependence plots, SHAP interaction values, and model performance plots.
• The library also provides example code for building the dashboard and documentation for implementing the various functions.
• The Explainer Dashboard also has a Heroku app that provides a well-laid-out high-level overview of some of the possible dashboards that you can create.
• The custom dashboard allows you to customize the layout and the look of the dashboard web application.

FAQ

Q: What is the Explainer Dashboard Python library? A: The Explainer Dashboard Python library is a powerful tool that allows you to deploy a dashboard web application for your machine learning models.

Q: What are some of the key features of the Explainer Dashboard? A: Some of the key features of the Explainer Dashboard include SHAP plots, permutation importance, partial dependence plots, SHAP interaction values, and model performance plots.

Q: How do I install the Explainer Dashboard? A: You can install the Explainer Dashboard using pip or conda.

Q: Does the Explainer Dashboard provide example code? A: Yes, the Explainer Dashboard provides example code for building the dashboard.

Q: Is there documentation available for the Explainer Dashboard? A: Yes, the documentation for the Explainer Dashboard provides some code that you can just copy and paste into your script files and run.

Q: Can I customize the layout and the look of the dashboard web application? A: Yes, the custom dashboard allows you to customize the layout and the look of the dashboard web application.