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Information Science, Business and Management., Organisation Theory
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.
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