# A Comparison of Machine Learning Approaches for Predicting Employee Attrition
## 5 Conclusions  
Employee attrition is a critical problem for several companies. In this paper we approach this issue through the use of data exploration techniques, and five machine learning  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Accuracy** & **Accuracy (Test)** & **F-Score (Test)** & **AUC-ROC (Test)** \\ \hline LR & 89.86 \(\pm\) 0.57 & **87.96 \(\pm\) 1.84** & 31.26 \(\pm\) 3.97 & **85.01 \(\pm\) 1.92** \\ CT & 88.81 \(\pm\) 0.04 & 84.29 \(\pm\) 2.06 & 16.03 \(\pm\) 3.33 & 69.33 \(\pm\) 4.18 \\ RF & 96.11 \(\pm\) 0.61 & 82.93 \(\pm\) 2.47 & **33.13 \(\pm\) 3.32** & 81.49 \(\pm\) 3.06 \\ NB & 68.57 \(\pm\) 0.76 & 67.82 \(\pm\) 2.50 & **33.78 \(\pm\) 2.70** & 75.90 \(\pm\) 3.83 \\ NN & **98.91 \(\pm\) 0.13** & 84.76 \(\pm\) 2.27 & 26.12 \(\pm\) 2.55 & 77.56 \(\pm\) 1.66 \\ Ensemble & 86.09 \(\pm\) 8.68 & 79.25 \(\pm\) 2.43 & 12.22 \(\pm\) 8.75 & 83.83 \(\pm\) 1.91 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance of the models on the dataset [9]. Results reported as mean and standard deviation on 5-fold cross validation. Best in **bold**.  
Figure 5: Variable importance by mean decrease in accuracy and Gini index for the Random Forest Model. The variables with higher values of mean decrease are the ones that impact the most the performance of the Random Forest model. Both the mean decrease in accuracy and the mean decrease in the Gini index are shown with a cutoff of 0.5. (**a**) Mean decrease in accuracy. (**b**) Mean decrease in Gini index.  
models trained on the IBM HR Analysis Employee Attrition dataset [9]. In our paper we show how machine learning models can indeed play a crucial role as decision support systems, identifying the best decisions to be made and providing data-motivated explanations. As further work we will employ data augmentation techniques for dealing with the dataset imbalance. Furthermore, we plan to use more advanced deep learning methods, such as graph neural networks (GNNs) [31, 32], to handle interactions between employees based on the hypothesis of homophily, that is, employees who are \"_closer_\" to each other are more likely to behave similarly. GNNs have been used in several real-world applications when there exists a relational structure between the entities under consideration [33, 34]. In addition, the collection of a dataset with a larger number of observations, further features, and--possibly--a relational structure could certainly lead to obtaining better results. Nonetheless applications of machine learning for predicting employee attrition could play a fundamental role in the context of managerial decisions. Companies could in fact decide to take actions for presenting new working opportunities as well as modifying negative working conditions, towards the idea of reducing employees' turnovers.  
Conceptualization, F.G. and G.M.D.; methodology, F.G. and G.M.D.; software, F.G.; validation, F.G. and G.M.D.; writing and review, F.G. and G.M.D.; supervision, G.M.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
M. Machine Learning LR Logistic Regression CT Classification Trees RF Random Forest MLP Multilayer Perceptron NN Neural Networks