Using elementary disturbances for testing of machine learning models A general method for testing of machine learning models based on elementary disturbances: An evaluation with image and audio data
Typ
Examensarbete för masterexamen
Program
Publicerad
2020
Författare
Hast, Arvid
Lindevall, Fredrik
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
This thesis explores the testing of machine learning models. The problem with
current testing methods is that testing often is case-specific and require significant
additional effort to perform. A novel method of adding simple elementary disturbances
to the input data is used. The method is done in a general way that should
work for different kinds of data and different types of machine learning models. The
simple disturbances can be used to predict how well a machine learning model handles
unseen disturbances. A general testing methodology could be useful as a simple
prediction of a machine learning model’s resilience to unseen disturbances.
Beskrivning
Ämne/nyckelord
Computer science , Software engineering , elementary , disturbance , machine learning , evaluation , testing , classification , image , audio