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
| dc.contributor.author | Hast, Arvid | |
| dc.contributor.author | Lindevall, Fredrik | |
| dc.contributor.department | Chalmers tekniska högskola / Institutionen för data och informationsteknik | sv |
| dc.contributor.examiner | Horkoff, Jennifer | |
| dc.contributor.supervisor | Feldt, Robert | |
| dc.date.accessioned | 2020-07-06T08:54:03Z | |
| dc.date.available | 2020-07-06T08:54:03Z | |
| dc.date.issued | 2020 | sv |
| dc.date.submitted | 2020 | |
| dc.description.abstract | 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. | sv |
| dc.identifier.coursecode | DATX05 | sv |
| dc.identifier.uri | https://hdl.handle.net/20.500.12380/301340 | |
| dc.language.iso | eng | sv |
| dc.setspec.uppsok | Technology | |
| dc.subject | Computer science | sv |
| dc.subject | Software engineering | sv |
| dc.subject | elementary | sv |
| dc.subject | disturbance | sv |
| dc.subject | machine learning | sv |
| dc.subject | evaluation | sv |
| dc.subject | testing | sv |
| dc.subject | classification | sv |
| dc.subject | image | sv |
| dc.subject | audio | sv |
| dc.title | 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 | sv |
| dc.type.degree | Examensarbete för masterexamen | sv |
| dc.type.uppsok | H |