Model Interpretation with Skater

Skater is a open source unified framework to enable Model Interpretation for all forms of model to help one build an Interpretable machine learning system often needed for real world use-cases. Skater supports algorithms to demystify the learned structures of a black box model both globally(inference on the basis of a complete data set) and locally(inference about an individual prediction).

The package was originally developed by Aaron Kramer, Pramit Choudhary and rest of the DataScience.com team to help data scientists and data enthusiast gain better model insight. Skater enables this vision by providing the ability to infer and debug the model’s decision policies as needed bringing “human in the loop”.

Release:1.1.1b4
Date:Sep 18, 2018

Indices and tables