A Churn Prediction Dataset from the Telecom Sector: A New Benchmark for Uplift Modeling

被引：0

作者：

Verhelst, Theo ^{[1
]}

Mercier, Denis ^{[2
]}

Shestha, Jeevan ^{[2
]}

Bontempi, Gianluca ^{[1
]}

机构：

[1] Univ Libre Bruxelles, Machine Learning Grp, Brussels, Belgium

[2] Orange Belgium, Data Sci Team, Brussels, Belgium

来源：

MACHINE LEARNING AND PRINCIPLES AND PRACTICE OF KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2023, PT IV | 2025年 / 2136卷

关键词：

D O I：

10.1007/978-3-031-74640-6_21

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Uplift modeling, also known as individual treatment effect (ITE) estimation, is an important approach for data-driven decision making that aims to identify the causal impact of an intervention on individuals. This paper introduces a new benchmark dataset for uplift modeling focused on churn prediction, coming from a telecom company in Belgium. Churn, in this context, refers to customers terminating their subscription to the telecom service. This is the first publicly available dataset offering the possibility to evaluate the efficiency of uplift modeling on the churn prediction problem. Moreover, its unique characteristics make it more challenging than the few other public uplift datasets.

引用

页码：292 / 299

页数：8

共 30 条

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