Shapash

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🔍 Overview

Shapash is a Python library designed to make machine learning interpretable and comprehensible for everyone. It offers various visualizations with clear and explicit labels that are easily understood by all.

With Shapash, you can generate a Webapp that simplifies the comprehension of interactions between the model's features, and allows seamless navigation between local and global explainability. This Webapp enables Data Scientists to effortlessly understand their models and share their results with both data scientists and non-data experts.

Additionally, Shapash contributes to data science auditing by presenting valuable information about any model and data in a comprehensive report.

Shapash is suitable for Regression, Binary Classification and Multiclass problems. It is compatible with numerous models, including Catboost, Xgboost, LightGBM, Sklearn Ensemble, Linear models, and SVM. For other models, solutions to integrate Shapash are available; more details can be found here.

[!NOTE] If you want to give us feedback : Feedback form

Shapash App Demo

<p align="center"> <img src="https://raw.githubusercontent.com/MAIF/shapash/master/docs/_static/shapash_global.gif" width="800"> </p>

🌱 Documentation and resources

• Readthedocs:
• Video presentation for french speakers
• Medium:
  • Understand your model with Shapash - Towards AI
  • Model auditability - Towards DS
  • Group of features - Towards AI
  • Building confidence on explainability - Towards DS
  • Picking Examples to Understand Machine Learning Model
  • Enhancing Webapp Built-In Features for Comprehensive Machine Learning Model Interpretation

🎉 What's new ?

| Version | New Feature | Description | Tutorial | |:-------------:|:-------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------------------------------:|:--------:| | 2.3.x | Additional dataset columns <br> New demo <br> Article | In Webapp: Target and error columns added to dataset and possibility to add features outside the model for more filtering options | <img src="https://raw.githubusercontent.com/MAIF/shapash/master/docs/_static/add_column_icon.png" width="50" title="add_column"> | 2.3.x | Identity card <br> New demo <br> Article | In Webapp: New identity card to summarize the information of the selected sample | <img src="https://raw.githubusercontent.com/MAIF/shapash/master/docs/_static/identity_card.png" width="50" title="identity"> | 2.2.x | Picking samples <br> Article | New tab in the webapp for picking samples. The graph represents the "True Values Vs Predicted Values" | <img src="https://raw.githubusercontent.com/MAIF/shapash/master/docs/_static/picking.png" width="50" title="picking"> | 2.2.x | Dataset Filter <br> | New tab in the webapp to filter data. And several improvements in the webapp: subtitles, labels, screen adjustments | <img src="https://raw.githubusercontent.com/MAIF/shapash/master/docs/_static/webapp.png" width="50" title="webapp"> | 2.0.x | Refactoring Shapash <br> | Refactoring attributes of compile methods and init. Refactoring implementation for new backends | <img src="https://raw.githubusercontent.com/MAIF/shapash/master/docs/_static/modular.png" width="50" title="modular"> | 1.7.x | Variabilize Colors <br> | Giving possibility to have your own colour palette for outputs adapted to your design | <img src="https://raw.githubusercontent.com/MAIF/shapash/master/docs/_static/variabilize-colors.png" width="50" title="variabilize-colors"> | 1.6.x | Explainability Quality Metrics <br> Article | To help increase confidence in explainability methods, you can evaluate the relevance of your explainability using 3 metrics: Stability, Consistency and Compacity | <img src="https://raw.githubusercontent.com/MAIF/shapash/master/docs/_static/quality-metrics.png" width="50" title="quality-metrics"> | 1.4.x | Groups of features <br> Demo | You can now regroup features that share common properties together. <br>This option can be useful if your model has a lot of features. | <img src="https://raw.githubusercontent.com/MAIF/shapash/master/docs/_static/groups_features.gif" width="120" title="groups-features"> | | 1.3.x | Shapash Report <br> Demo | A standalone HTML report that constitutes a basis of an audit document. | <img src="https://raw.githubusercontent.com/MAIF/shapash/master/docs/_static/report-icon.png" width="50" title="shapash-report"> |

🔥 Features

• Display clear and understandable results: plots and outputs use explicit labels for each feature and its values

<p align="center"> <img align="left" src="https://github.com/MAIF/shapash/blob/master/docs/_static/shapash-grid-images-02.png?raw=true" width="28%"/> <img src="https://github.com/MAIF/shapash/blob/master/docs/_static/shapash-grid-images-06.png?raw=true" width="28%" /> <img align="right" src="https://github.com/MAIF/shapash/blob/master/docs/_static/shapash-grid-images-04.png?raw=true" width="28%" /> </p> <p align="center"> <img align="left" src="https://github.com/MAIF/shapash/blob/master/docs/_static/shapash-grid-images-01.png?raw=true" width="28%" /> <img src="https://github.