Metadata-Version: 1.2
Name: explainerdashboard
Version: 0.2.5
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: 
        
        This package makes it convenient to quickly deploy a dashboard web app
        that explains the workings of a (scikit-learn compatible) fitted machine 
        learning model. The dashboard provides interactive plots on model performance, 
        feature importances, feature contributions to individual predictions, 
        partial dependence plots, SHAP (interaction) values, visualisation of individual
        decision trees, etc. 
        
        
        The goal is manyfold:
        
            - Make it easy for data scientists to quickly inspect the inner workings and 
                performance of their model with just a few lines of code
            - Make it possible for non data scientist stakeholders such as managers, 
                directors, internal and external watchdogs to interactively inspect 
                the inner workings of the model without having to depend on a data 
                scientist to generate every plot and table
            - Make it easy to build a custom application that explains individual 
                predictions of your model for customers that ask for an explanation
            - Explain the inner workings of the model to the people working with 
                model in a human-in-the-loop deployment so that they gain understanding 
                what the model does and doesn't do. 
                This is important so that they can gain an intuition for when the 
                model is likely missing information and may have to be overruled.
        
        The dashboard includes:
        
            - SHAP values (i.e. what is the contribution of each feature to each 
                individual prediction?)
            - Permutation importances (how much does the model metric deteriorate 
                when you shuffle a feature?)
            - Partial dependence plots (how does the model prediction change when 
                you vary a single feature?
            - Shap interaction values (decompose the shap value into a direct effect 
                an interaction effects)
            - For Random Forests and xgboost models: visualization of individual trees
                in the ensemble.  
            - Plus for classifiers: precision plots, confusion matrix, ROC AUC plot, 
                PR AUC plot, etc
            - For regression models: goodness-of-fit plots, residual plots, etc.
        
        The library is designed to be modular so that it is easy to design your 
        own custom dashboards so that you can focus on the layout and project specific 
        textual explanations of the dashboard. (i.e. design it so that it will be 
        interpretable for business users in your organization, not just data scientists)
        
        
        A deployed example can be found at http://titanicexplainer.herokuapp.com
        
Keywords: machine learning,explainability,shap,feature importances,dash
Platform: UNKNOWN
Classifier: Development Status :: 3 - 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
