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A comparison between Neural networks, Lasso regularized Logistic regression, and Gradient boosted trees in modeling binary sales
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory.
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
En jämförelse mellan Neurala nätverk, Lasso regulariserad Logistisk regression, och Gradient boostade träd för modellering av binära försäljningar (Swedish)
Abstract [en]

The primary purpose of this thesis is to predict whether or not a customer will make a purchase from a specific item category. The historical data is provided by the Nordic online-based IT-retailer Dustin. The secondary purpose is to evaluate how well a fully connected feed forward neural network performs as compared to Lasso regularized logistic regression and gradient boosted trees (XGBoost) on this task. This thesis finds XGBoost to be superior to the two other methods in terms of prediction accuracy, as well as speed.

Abstract [sv]

Det primära syftet med denna uppsats är att förutsäga huruvida en kund kommer köpa en specifik produkt eller ej. Den historiska datan tillhandahålls av den Nordiska internet-baserade IT-försäljaren Dustin. Det sekundära syftet med uppsatsen är att evaluera hur väl ett djupt neuralt nätverk presterar jämfört med Lasso regulariserad logistisk regression och gradient boostade träd (GXBoost). Denna uppsats fann att XGBoost presterade bättre än de två andra metoderna i såväl träffsäkerhet, som i hastighet.

Place, publisher, year, edition, pages
2019.
Series
TRITA-SCI-GRU ; 2019:084
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:kth:diva-252556OAI: oai:DiVA.org:kth-252556DiVA, id: diva2:1319871
External cooperation
Dustin
Subject / course
Mathematical Statistics
Educational program
Master of Science - Applied and Computational Mathematics
Supervisors
Examiners
Available from: 2019-06-04 Created: 2019-06-03 Last updated: 2019-06-04Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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  • de-DE
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  • nn-NB
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  • Other locale
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Output format
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