v0.2.5

version 0.2.5:

New Features

• 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

Other Changes

• removed monkeypatching shap_explainer note