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  • 2020
  • Working Paper
  • HBS Working Paper Series

Machine Learning for Pattern Discovery in Management Research

By: Prithwiraj Choudhury
  • Format:Print
  • | Language:English
  • | Pages:44
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Abstract

Supervised machine learning (ML) methods are a powerful toolkit for discovering robust patterns in quantitative data. The patterns identified by ML could be used as an observation for further inductive or abductive research, but should not be treated as the result of a deductive causal test. To demonstrate the application of ML for pattern discovery, we implement ML algorithms to study employee turnover at a large technology company. We interpret the relationships between variables using partial dependence plots, which uncover surprising patterns that may have gone unnoticed using traditional methods. To guide readers evaluating such pattern discovery, we provide standards for evaluating model performance, illustrate human decisions in the process, and warn of common misinterpretation pitfalls. An online appendix provides code and data to implement the algorithms demonstrated in the paper.

Keywords

Machine Learning; Theory Building; Induction; Decision Trees; Random Forests; K-nearest Neighbors; Neural Network; P-hacking; Analytics and Data Science; Analysis

Citation

Choudhury, Prithwiraj, Ryan Allen, and Michael G. Endres. "Machine Learning for Pattern Discovery in Management Research." Harvard Business School Working Paper, No. 19-032, September 2018. (Revised June 2020.)
  • SSRN

About The Author

Prithwiraj Choudhury

Technology and Operations Management
→More Publications

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More from the Author
  • The World Is Your Office: How Work from Anywhere Boosts Talent, Productivity, and Innovation By: Prithwiraj Choudhury
  • Location-Specificity and Relocation Incentive Programs for Remote Workers By: Thomaz Teodorovicz, Prithwiraj Choudhury and Evan Starr
  • Enerjisa Üretim: The Digital Era of Electricity Generation By: Prithwiraj Choudhury and Sadika El Hariri
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