How to Measure Explainability and Interpretability of Machine Learning Results

Research output: Chapter in Book/Report/Conference proceedings › Chapter › peer-review

Original language	English (American)
Title of host publication	Genetic Programming Theory and Practice XXI
Publisher	Springer
Pages	357-374
ISBN (Electronic)	978-981-96-0077-9
ISBN (Print)	978-981-96-0076-2
Publication status	Published - 28 Feb 2025

Name	Genetic and Evolutionary Computation
Publisher	Springer
ISSN (Print)	1932-0167
ISSN (Electronic)	1932-0175

Cite this

@inbook{1b7408d110694f7fa2809a4f278ab365,

title = "How to Measure Explainability and Interpretability of Machine Learning Results",

author = "Mayrhuber, \{Elisabeth Maria\} and Stephan Winkler and Bogdan Burlacu",

year = "2025",

month = feb,

day = "28",

language = "Englisch (Amerika)",

isbn = "978-981-96-0076-2",

series = "Genetic and Evolutionary Computation",

publisher = "Springer",

pages = "357--374",

booktitle = "Genetic Programming Theory and Practice XXI",

address = "Deutschland",

}