Metadata-Version: 1.2
Name: explainerdashboard
Version: 0.1.8.1
Summary: explainerdashboard allows you quickly build an interactive dashboard to explain the inner workings of your machine learning model.
Home-page: https://github.com/oegedijk/explainerdashboard
Author: Oege Dijk
Author-email: oegedijk@gmail.com
License: MIT
Project-URL: Github page, https://github.com/oegedijk/explainerdashboard/
Project-URL: Documentation, https://explainerdashboard.readthedocs.io/
Description: 
        explainerdashboard allows you quickly build an interactive dashboard to explain the inner workings of your machine learning model.
        The library is flexible in that you first create an ExplainerBunch class that handles the computations and 
        plotting functionality for you, so that you can then build a plotly dash dashboard on top of that. 
        
        The standard built-in dashboard comes with a number of standard tabs (that can be switched on individually), namely:
        
        - Model Summary Tab (classifier/regression metrics and plots + feature importances)
        - Contributions Tab (explain individual predictions, and compare what-if scenarios using pdp plots)
        - Dependence Tab (investigate how predictions change along the axis of each feature)
        - Interactions Tab (investigate the interaction effects between your variables)
        - Shadow Trees Tab (for RandomForests, display all the individual trees inside the forest)
        
        It includes:
        - Model summary statistics
        - SHAP values (importances, individual contributions, dependence plots, interaction plots, etc)
        - permutation importances
        - partial dependence plots
        - DecisionTree visualizers
        
        You can display an interactive dashboard with all of these features with only three lines of code.
        
        A deployed example can be found at http://titanicexplainer.herokuapp.com
        
Keywords: machine learning,explainability,shap,feature importances,dash
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Environment :: Web Environment
Classifier: Framework :: Dash
Classifier: Framework :: Flask
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
