Comparing the Performance of Machine Learning Algorithms for Predicting Employees’ Turnover

Aldossary, Yasmeen; Ebrahim, Majeed; Alhaddad, Salman; Aldossary, Manar; Hilal, Sawsan

doi:10.1109/icdabi56818.2022.10041451

Cited by 1 publication

(1 citation statement)

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“…Another study on the same dataset found that Random Forest (RF) was the most effective model 86 . In the context of a big data company, RF outperformed other algorithms, including Logistic Regression, K-Nearest Neighbors (KNN), and Decision Tree, in modeling turnover 87 . A comparison involving Decision Tree, RF, k-nearest neighbor, and Naïve Bayes classifiers on a dataset consisting of 15,000 observations also identified RF as the best-performing model 78 .…”

Section: Similar Workmentioning

confidence: 99%

Explainable prediction of node labels in multilayer networks: a case study of turnover prediction in organizations

Gadár,

Abonyi

2024

Sci Rep

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In real-world classification problems, it is important to build accurate prediction models and provide information that can improve decision-making. Decision-support tools are often based on network models, and this article uses information encoded by social networks to solve the problem of employer turnover. However, understanding the factors behind black-box prediction models can be challenging. Our question was about the predictability of employee turnover, given information from the multilayer network that describes collaborations and perceptions that assess the performance of organizations that indicate the success of cooperation. Our goal was to develop an accurate prediction procedure, preserve the interpretability of the classification, and capture the wide variety of specific reasons that explain positive cases. After a feature engineering, we identified variables with the best predictive power using decision trees and ranked them based on their added value considering their frequent co-occurrence. We applied the Random Forest using the SMOTE balancing technique for prediction. We calculated the SHAP values to identify the variables that contribute the most to individual predictions. As a last step, we clustered the sample based on SHAP values to fine-tune the explanations for quitting due to different background factors.

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Section: Similar Workmentioning

confidence: 99%