Year: 2025 | Month: May | Volume: 12 | Issue: 5 | Pages: 546-562
DOI: https://doi.org/10.52403/ijrr.20250556
A Data-Driven Revolution in Performance Management: Harnessing the Power of Random Forest Algorithm
Sunil Basnet
Chief People and System officer, Virtuosway, Kathmandu, Bagmati province, Nepal
Corresponding Author: Sunil Basnet
ABSTRACT
In the ever-evolving landscape of organizational dynamics, traditional performance management methodologies struggle to meet the demands of objectivity, consistency, and predictive accuracy. Subjective evaluations, prone to biases and inconsistencies, often fall to capture the multifaceted nature of employee performance. This study explores the transformative potential of random forests, a machine learning algorithm, in revolutionizing performance management practices. By adding structured data sources encompassing past performance metrics, skills assessments, and feedback mechanisms, random forests offer a promising avenue for mitigating biases and enhancing the objectivity of performance evaluations. Through an in-depth investigation, this research explores the application of random forests in analyzing diverse datasets, identifying key performance indicators, and predicting future performance outcomes. The findings demonstrate the model’s exceptional accuracy, achieving a Mean Squared Error (MSE) of 0.00016, Mean Absolute Error (MAE) of 0.0091, and an R² score of 0.9999, significantly outperforming traditional evaluation methods. The ultimate aim is to develop a more objective, consistent, and insightful approach to performance evaluation, ultimately fostering employee development and organizational success in the modern work environment.
Keywords: Machine Learning (ML), Artificial Intelligence (AI), Random Forest Algorithm, Data Driven, Human Resource Management, Performance Management
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