pyCeterisParibus: explaining Machine Learning models with Ceteris Paribus Profiles in Python

By Michal Kuzba, Ewa Baranowska, Przemyslaw Biecek. 2019

16 citations recorded by the source corpus.

Machine learning is needed and used everywhere. It has fundamentally changed all datadriven disciplines, like health-care, biology, finance, legal, military, security, transportation, and many others. The increasing availability of large annotated data sources combined with recent developments in Machine Learning revolutionizes many disciplines. However, predictive models become more and more complex. It is not uncommon to have ensembles of predictive models with thousands or millions of parameters. Such models act as blackboxes. It is almost impossible for a human to understand reasons for model decisions.

Subfields: Artificial Intelligence

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In Artificial Intelligence

1. Alicja Gosiewska
2. Przemyslaw Biecek

Citations in the corpus

• Do Not Trust Additive Explanations (2019, methodology): “In this method, local sampling is based on decision trees and Ceteris Paribus Profiles (Kuzba et al., 2019).”