Comparing Variable Importance in Prediction of Silence Behaviours between Random Forest and Conditional Inference Forest Models.

Authors

Stephen Barrett Dr, Technological University DublinFollow
Geraldine Gray Dr, Technological University DublinFollow
Colm McGuinness Dr, Technological University DublinFollow
Michael Knoll Dr., Universität LeipzigFollow

Document Type

Article

Rights

Available under a Creative Commons Attribution Non-Commercial Share Alike 4.0 International Licence

Disciplines

Information Science, Business and Management., Organisation Theory

Publication Details

DATA ANALYTICS 2020, The Ninth International Conference on Data Analytics

Available on ThinkMind: http://www.thinkmind.org/index.php?view=article&articleid=data_analytics_2020_2_30_60033

IARIA XPS Press

Abstract

This paper explores variable importance metrics of Conditional Inference Trees (CIT) and classical Classification And Regression Trees (CART) based Random Forests. The paper compares both algorithms variable importance rankings and highlights why CIT should be used when dealing with data with different levels of aggregation. The models analysed explored the role of cultural factors at individual and societal level when predicting Organisational Silence behaviours.

DOI

https://doi.org/10.21427/9rsv-0479

Recommended Citation

Barrett, S., Gray, G., & McGuinness, C. (2020). Comparing Variable Importance in Prediction of Silence Behaviours Between Random Forest and Conditional Inference Forest Models. DATA ANALYTICS 2020: 9th International Conference on Data Analytics pg. 28-34. doi:10.21427/9rsv-0479