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New dashboard tab: WhatIfComponent/WhatIfComposite/WhatIfTab: allows you
to explore whatif scenario's by editing multiple features and observing
shap contributions and pdp plots. Switch off with ExplainerDashboard
parameter whatif=False.
New login functionality: you can restrict access to your dashboard by passing
a list of [login, password] pairs: ExplainerDashboard(explainer, logins=[['login1', 'password1'], ['login2', 'password2']]).run()
Added 'target' parameter to explainer, to make more descriptive plots.
e.g. by setting target='Fare', will show 'Predicted Fare' instead of
simply 'Prediction' in various plots.
in detailed shap/interaction summary plots, can now click on single
shap value for a particular feature, and have that index highlighted
for all features.
autodetecting Google colab environment and setting mode='external'
(and suggesting so for jupyter notebook environments)
confusion matrix now showing both percentage and counts
Added classifier model performance summary component
Added cumulative precision component
Improvements
added documentation on how to deploy to heroku
Cleaned up modebars for figures
ClassifierExplainer asserts predict_proba attribute of model
with model_output='logodds' still display probability in prediction summary