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Evaluation of machine learning methods to predict payment preferences
KTH, School of Electrical Engineering and Computer Science (EECS).
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Utvärdering av maskininlärningsmetoder för att förutsäga betalningspreferenser (Swedish)
Abstract [en]

The last couple of years machine learning has seen a renaissance, with Artificial Neural Networks in particular rising to prominence. The technology is being adopted by more and more businesses, with varying degrees of success. Klarna has already been experimenting with machine learning to predict payment preferences, however currently a hybrid between ad-hoc rules and a random forest model is being used in production. This report aims to find out if a pure machine learning algorithm can outperform a hybrid system for this purpose. To achieve this, four methods were tested; Random Forest, Artificial Neural Net- work, Support Vector Machine and Logistic Regression model. Three of these models outperformed the model in production. Best of these were the Artificial Neural Network which, with a cutoff threshold designed to achieve the same precision, achieved 10 percentage points higher recall. By combining the probabilities produced by an Artificial Neural Network and a Random Forest, even better results could be achieved. That method achieved 11.5 percentage points higher recall than production results with the same precision. It could be shown that the two methods had different strengths and were good at classifying different examples.

Abstract [sv]

Explosionen av maskininlärning, och Artificiella Neurala Nätverk i synnerhet, har resulterat i att tekniken appliceras på allt fler användningsområden. Klarna har redan experimenterat med maskininlärning för att förutsäga betalmetoder, men för närvarande används en hybrid av regler och en Random-Forest modell. Denna rapport ämnar att utreda om en ren maskininlärningsmetod kan överträffa den nuvarande hybridmetoden. För att göra detta testades fyra olika metoder, Random Forest, Neurala Nätverk, Support Vector Machines och Logistic Regression. Det visade sig att tre av dessa presterade bättre än modellen i produktion. Bäst av alla metoder var Neurala Nätverk som var 10 procentenheter bättre än modellen i produktion i recall, med samma precision. Genom att kombinera sannolikheterna från en Random Forest samt ett Neuralt Nätverk kunde ännu bättre resultat uppnås, 11.5 procentenheter bättre i recall än modellen i produktion till samma precision.

Place, publisher, year, edition, pages
2019. , p. 53
Series
TRITA-EECS-EX ; 2019:594
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-264504OAI: oai:DiVA.org:kth-264504DiVA, id: diva2:1373851
External cooperation
Klarna
Educational program
Master of Science in Engineering - Industrial Engineering and Management
Supervisors
Examiners
Available from: 2019-11-28 Created: 2019-11-28

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