Model Explainer Dashboard

This package makes it convenient to quickly deploy a dashboard web app that explains the workings of a (scikit-learn compatible) machine learning model. The dashboard provides interactive plots on model performance, feature importances, feature contributions to individual predictions, “what if” analysis, partial dependence plots, SHAP (interaction) values, visualisation of individual decision trees, etc.

You can also interactively explore components of the dashboard in a notebook/colab environment (or just launch a dashboard straight from there). Or design a dashboard with your own custom layout and explanations (thanks to the modular design of the library). And you can combine multiple dashboards into a single ExplainerHub.

Dashboards can be exported to static html directly from a running dashboard, or programmatically as an artifact as part of an automated CI/CD deployment process.

Works with scikit-learn, xgboost, catboost, lightgbm, and skorch (sklearn wrapper for tabular PyTorch models) and others.

Installation

You can install the package through pip:

pip install explainerdashboard
or conda-forge:
conda install -c conda-forge explainerdashboard

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FAQ's

A machine learning dashboard is a visual interface that displays key information about a machine learning model. It can include various elements like:
- Model performance metrics: Accuracy, precision, recall, and other metrics that measure the model's effectiveness.
- Feature importance: Shows which features in your data have the most significant influence on the model's predictions.
- Individual prediction explanations: Explains why the model made a specific prediction for a particular data point.
- Visualizations: Charts and graphs to help you understand the model's behavior and identify potential biases.

- The Model Explainer Dashboard is a specific software tool designed to help you understand and explain the predictions made by your machine learning models. It focuses on providing clear visualizations and interactive elements to explore how features within your data contribute to the model's outputs.

- Improved debugging and troubleshooting: By visualizing model behavior, you can identify potential biases or errors in your model's predictions.
- Enhanced model interpretability: The dashboard helps you understand why a model makes certain predictions, fostering trust and transparency.
- Effective communication of model results: Dashboards provide a clear and concise way to communicate model performance and insights to stakeholders who may not be technical experts.

- The level of ease of use depends on your existing familiarity with machine learning concepts. The dashboard itself might require some technical understanding to set up and interpret the visualizations. However, the goal is to provide a user-friendly interface compared to manually analyzing complex model outputs.

- Data scientists: The dashboard is a valuable tool for data scientists to gain deeper understanding of their models and improve their performance.
- Machine learning engineers: It can aid in debugging, identifying potential biases, and optimizing model deployment.
- Business analysts and stakeholders: The clear visualizations can help communicate the model's functionality and decision-making process to non-technical audiences.

- The specific functionalities of the Model Explainer Dashboard might vary depending on the tool's design. It's likely to work well with common machine learning models like decision trees, random forests, and linear regression. More complex models like deep neural networks might require additional configurations or might not be fully supported by all explainer dashboards.

By explaining your model's predictions, the Model Explainer Dashboard can help you:
- Gain trust in your model: Understand how the model is making decisions and ensure it aligns with your expectations.
- Identify potential biases: Uncover any hidden biases present in the data or model that might be influencing predictions.
- Improve model performance: By understanding feature importance, you can identify areas for improvement and potentially refine your model.
- Debug and troubleshoot issues: Identify any errors or unexpected behavior in your model through explanations for individual predictions.

- Best practices include ensuring the dashboard is user-friendly and intuitive, incorporating feedback from end-users during the design process, providing comprehensive documentation and tutorials, regularly updating the dashboard with new features and improvements, and ensuring data privacy and security.

- Limitations may include the complexity of interpreting certain types of models, such as deep learning models, the potential for overfitting or bias in model explanations, and the need for domain expertise to interpret and contextualize the insights provided by the dashboard.

- Key features of an explainer dashboard include interactive visualizations of model predictions, feature importance rankings, partial dependence plots, individual instance explanations, model performance metrics, and data summaries.

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