Darwen, Paul J. (2023) Direction of the Difference between Bayesian Model Averaging and the Best-Fit Model on Scarce-Data Low-Correlation Churn Prediction. In: Lecture Notes in Artificial Intelligence (13995) pp. 210-223. From: ACIIDS 2023: 15th Asian Conference on Intelligent Information and Database Systems, 24-26 July 2023, Phuket, Thailand.

PDF (Published Version) - Published Version Restricted to Repository staff only

Abstract

On a scarce-data customer churn prediction problem, using the tiny differences between the predictions of (1) the single-best model and (2) the ensemble from Bayesian model averaging, gives greater accuracy than state-of-the-art approaches such as XGBoost. The proposed approach reflects the cost-benefit aspect of many such problems: for customer churn, incentives to stay are expensive, so what's needed is a short list of customers with a high probability of churning. It works even though in every test case, the predicted outcome is always the same from both the best-fit model and Bayesian model averaging. The approach suits many scarce-data prediction problems in commerce and medicine.

Item ID:	80432
Item Type:	Conference Item (Research - E1)
ISBN:	9789819958344
Keywords:	ensembles, customer churn, small data, scarce data, Bayesian model averaging, committee of committees, cold start problem
Copyright Information:	© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023
Date Deposited:	13 Sep 2023 23:37
FoR Codes:	46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461199 Machine learning not elsewhere classified @ 90% 35 COMMERCE, MANAGEMENT, TOURISM AND SERVICES > 3506 Marketing > 350601 Consumer behaviour @ 10%
SEO Codes:	22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220402 Applied computing @ 10% 22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence @ 90%
Downloads:	Total: 1
	More Statistics