Purpose

This study aims to develop a model to predict non-violent work behaviour (NVWB) among employees using machine learning techniques.

Design/methodology/approach

Four machine learning techniques (Naïve Bayes, decision tree, logistic regression and ensemble learning) were used to develop a prediction model for NVWB of employees. Also, 10-fold cross-validation method was used to validate the NVWB prediction models. The confusion matrix is used to derive various performance matrices to express the predictive capability of NVWB models quantitatively.

Findings

The model developed using random forest technique was identified as best NVWB prediction model, as it resulted in highest true positive rate and true negative rate, thereby resulting in the highest geometric mean, balance and area under receiver operator characteristics curve.

Originality/value

To the best of the authors’ knowledge, this is one of the pioneer studies that used machine learning techniques to develop a predictive model of NVBW.

中文翻译：

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使用机器学习技术预测员工的非暴力工作行为

目的

本研究旨在开发一个模型，利用机器学习技术预测员工的非暴力工作行为 (NVWB)。

设计/方法论/途径

使用四种机器学习技术（朴素贝叶斯、决策树、逻辑回归和集成学习）来开发员工 NVWB 的预测模型。此外，还使用10倍交叉验证方法来验证NVWB预测模型。利用混淆矩阵推导各种性能矩阵，定量表达NVWB模型的预测能力。

发现

使用随机森林技术开发的模型被认为是最佳的 NVWB 预测模型，因为它产生了最高的真阳性率和真阴性率，从而产生了最高的几何平均值、平衡和接收者操作员特征曲线下面积。

原创性/价值

据作者所知，这是使用机器学习技术开发 NVBW 预测模型的先驱研究之一。