Performance of Various Naive Bayes Algorithms on Employee Attrition Detection

Year 2025, Volume: 2 Issue: 1, 1 - 6, 01.07.2025

Fahriye Gemci

Abstract

Since companies aim to increase profits, the company's resources must be used correctly. Two of the resources that human resources should consider important for the company are time and the continuation of talented employees in the workplace. In the case of loss of talented employees, training given to new workers requires wages and time. This situation shows that loss of employees is an important problem in company policy.
This study aims to automatically detect employee attrition with machine learning algorithms. Naive Bayes classifiers are one of the successful machine learning algorithms used in many different fields such as text processing. Therefore, in this study, supervised classification has performed on the dataset with 5 different Naive Bayes algorithms in determining the employment loss. Gaussian Naive Bayes and Categorical Naive Bayes show the most successful results in determining the loss of workers.

Keywords

• Naïve Bayes, • Gaussion Naïve Bayes, • Categorical Naïve Bayes, • Employee Attrition, • Supervised Classifier

Çeşitli Naive Bayes Algoritmalarının Çalışan Kaybı Tespiti Üzerindeki Performansı

Abstract

Şirketler karı artırmayı amaçladığından, şirketin kaynaklarının doğru kullanılması gerekir. İnsan kaynaklarının şirket için önemli düşünmesi gereken kaynaklarından ikisi, zaman ve yetenekli işçileri iş yerinde devam ettirilmesidir. Yetenekli işçi kaybında, yeni işçiye verilen eğitimler ücret ve zaman gerektirmektedir. Bu durum işçi kaybının şirket politikasında önemli bir sorun olduğunu göstermektedir.
Bu çalışmada işçi istihdam kaybını makine öğrenme algoritmaları ile otomatik olarak tespit etmek amaçlanmaktadır. Naif Bayes sınıflandırıcıları, metin işleme gibi birçok farklı alanda kullanılan başarılı makine öğrenimi algoritmalarından biridir. Bu nedenle, bu çalışmada, istihdam kaybını belirlemede veri kümesinde 5 farklı Naif Bayes algoritması ile gözetimli sınıflandırma gerçekleştirilmiştir. İşçi kayıplarının belirlenmesinde Gauss Saf Bayes ve Kategorik Saf Bayes en başarılı sonuçları göstermektedir.

Keywords

• Saf Bayes, • Çalışan Kaybı, • Gaussion Saf Bayes, • Kategorik Saf Bayes, • Denetimli Sınıflandırıcı

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