Skater: Python Library for Model Interpretation/Explanations. Skater is a 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(** we are actively working towards to enabling faithful interpretability for all forms models). It is an open source python library designed 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 project was started as a research idea to find ways to enable better interpretability(preferably human interpretability) to predictive ”black boxes” both for researchers and practioners. The project is still in beta phase.
Keywords for this software
References in zbMATH (referenced in 2 articles )
Showing results 1 to 2 of 2.
- Arya, Vijay; Bellamy, Rachel K. E.; Chen, Pin-Yu; Dhurandhar, Amit; Hind, Michael; Hoffman, Samuel C.; Houde, Stephanie; Liao, Q. Vera; Luss, Ronny; Mojsilović, Aleksandra; Mourad, Sami; Pedemonte, Pablo; Raghavendra, Ramya; Richards, John T.; Sattigeri, Prasanna; Shanmugam, Karthikeyan; Singh, Moninder; Varshney, Kush R.; Wei, Dennis; Zhang, Yunfeng: AI Explainability 360: an extensible toolkit for understanding data and machine learning models (2020)
- Kacper Sokol; Alexander Hepburn; Rafael Poyiadzi; Matthew Clifford; Raul Santos-Rodriguez; Peter Flach: FAT Forensics: A Python Toolbox for Implementing and Deploying Fairness, Accountability and Transparency Algorithms in Predictive Systems (2020) not zbMATH