explainerdashboard

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 co-workers,
    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 do and does not 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