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Rolling out DiCE for sklearn and regression models
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@amit-sharma amit-sharma released this 01 Mar 15:26
v0.5
ee4b2f4
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Learn about vigilant mode.
  • [Major] DiCE now supports sklearn models. Added three model-agnostic methods: randomized, genetic algorithm, and kd-tree
  • [Major] Support for regression and multi-class problems
  • [Major] Added local and global feature importance scores based on counterfactuals
  • [Major] Better support for customizing counterfactuals through features_to_vary and permitted_range parameters for both continuous and categorical features
  • [Refactor] ML Model and DiCE Explainer can use different feature transformations. Model's transformation can be provided as an input to the dice_ml.Model constructor. DiCE accepts inputs in the original data frame and does its transformations internally
  • Enhanced tests for the library
  • Deep learning libraries (tensorflow and pytorch) marked as optional dependencies
  • New notebooks showing applications of DiCE in docs/source/notebooks/

A big thanks to @raam93, @soundarya98 and @gaugup for this release!

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