Less is more: improving customer churn prediction with split training data in the Penalized Logit Leaf Model

Additional Metadata
Keywords	Insurance; churn; Logit leaf model; penalized logit leaf model; honest estimation; probability calibration; elastic net
Thesis Advisor	O'Neill, EP
Persistent URL	hdl.handle.net/2105/65851
Series	Econometrie
Organisation	Erasmus School of Economics
Citation APA Style AAA Style APA 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	Hendriks, M.L.F. (2022, September 8). Less is more: improving customer churn prediction with split training data in the Penalized Logit Leaf Model. Econometrie. Retrieved from http://hdl.handle.net/2105/65851

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