- make machine
- learning models
- transparent and
- understandable
- by everyone
Shapash is a Python library dedicated to the interpretability of Data Science models. It provides several types of visualization that display explicit labels that everyone can understand. Data Scientists can more easily understand their models, share their results and easily document their projects in a html report. End users can understand the suggestion proposed by a model using a summary of the most influential criteria.
Features
- Compatible with Shap and Lime
- Uses shap backend to display results in a few lines of code
- Encoders objects and features dictionaries used for clear results
- Compatible with category_encoders & Sklearn ColumnTransformer
- Visualizations of global and local explainability
- Webapp to easily navigate from global to local
- Summarizes local explanation
- Offers several parameters in order to summarize in the most suitable way for your use case
- Exports your local summaries to a Pandas DataFrame
- Usable for Regression, Binary Classification or Multiclass
- Compatible with most of sklearn, lightgbm, catboost, xgboost models
- Relevant for exploration and also deployment (through an API or in Batch mode)
- Freeze different aspects of a data science project as a basis of an audit report
- Regroup features that share common properties together
- Explainability Quality Metrics to increase confidence in explainability methods
- Select subsets for further analysis of explainability by filtering on explanatory and additional features, correct or wrong predictions
easy to set up
Provide a SmartExplainer class to understand your model and summarize explanation with a simple syntax
high adaptability
Very few arguments are required to display results. But the more you work on cleaning and documenting the data, the clearer the results will be for the end user
