Maskinlæring som politologisk værktøj

Research output: Contribution to journalJournal articleResearchpeer-review

The use of machine learning is rapidly gaining ground in empirical political science and public policy making. Machine learning can be employed to predict individual-level outcomes and thus holds potential for increasing precision in targeted early interventions across various policy domains. This article introduces machine learning as a part of the political science and public policy tool-box. It explains key concepts and outlines how machine learning can be carried out in practice. A decision tree model used to predict dropout among students at Copenhagen University College serves as an illustrative case throughout the article. Lastly, the article discusses some of the methodological promises and pitfalls of using machine learning in a social science setting.
Original languageDanish
JournalPolitica
Volume51
Issue number2
Pages (from-to)168-186
Number of pages19
Publication statusPublished - 2019

ID: 234081808