Comparing the Performance of XGBoost and Shapley Values to Last Touch Attribution

Additional Metadata
Keywords	Attribution modeling; Machine Learning; Last Touch Attribution; XGBoost; GPBoost; Shapley Values; SHAP; Linear Mixed Effects Logistic Regression; Uniform Attribution; Variable Importance; Explainable Machine Learning; Conversion; Touchpoint Attribution; Digital Marketing.
Thesis Advisor	Gruber, K
Persistent URL	hdl.handle.net/2105/66661
Series	Business Economics
Organisation	Erasmus School of Economics
Citation APA APA Style APA-ALL Style AAA Style Cell Style Chicago Style Harvard Style IEEE Style MLA Style Nature Style Vancouver Style American-Institute-of-Physics Style Council-of-Science-Editors Style BibTex Format Endnote Format RIS Format CSL Format DOIs only Format	Tout, J. (2023). Comparing the Performance of XGBoost and Shapley Values to Last Touch Attribution. In Business Economics.http://hdl.handle.net/2105/66661

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