Tutorials

This part offers a series of tutorials and allows users to gradually discover the different features of Shapash

• overview
  • Start the Shapash Web App on a sample dataset
  • Shapash in Jupyter - Overview
  • Shapash model in production - Overview
• common
  • Groups of features
  • Use groups of features in production with SmartPredictor object
  • Shapash with custom colors
• explainability_quality
  • Building confidence on explainability methods
• explainer_and_backend
  • Compute Contributions with Shap - Summarize Them With Shapash
  • Using Shapash with Lime explainer - Titanic
  • Compile faster Lime and consistency of contributions
  • Using Shapash with FastTreeSHAP explainer
  • Tutorial Shapash Backend
• generate_report
  • Shapash Report
• generate_webapp
  • Add features outside of the model for more exploration options
• plots_and_charts
  • How to use filter and local_plot methods
  • Contributions plot
  • Features importance
  • Contributions comparing plot
  • Interactions plot
  • scatter_plot_prediction
• postprocess
  • Postprocessing parameter in compile method
  • Compiling without postprocessing parameter
  • Compiling with postprocessing parameter
• predictor_to_production
  • From model training to deployment - an introduction to the SmartPredictor object
• use_encoders
  • Category_encoder tutorial
  • ColumnTransformer tutorial
  • Dictionnary Encoding tutorial