Comparative analysis of random forest and ThymeBoost models in machine learning
Anand Ranjan, Sanjay Kumar, Siddharth Singh, Deepak Kumar Gupta · 2024
Machine learning (ML) has become a pivotal technology in various domains, making it essential to evaluate and compare different models for predictive accuracy and performance. In this paper, we conduct a comprehensive comparative analysis of two prominent ML models, Random Forest and ThymeBoost. The aim of this research is to evaluate how well they perform and how suitable they are for different applications within the ML domain. The comparative analysis encompasses various dimensions, including predictive accuracy, computational efficiency, interpretability, and robustness. We employ a diverse set of benchmark datasets and evaluation metrics to provide a holistic view of model performance. Our findings reveal the strengths and weaknesses of Random Forest and ThymeBoost in different contexts, shedding light on their respective capabilities and limitations. These insights will assist practitioners and researchers in making informed decisions when selecting a machine learning model based on the specific requirements of their applications. Through a systematic examination of Random Forest and ThymeBoost, this study contributes to the ongoing dialogue about the utility of ML models in real-world scenarios. It acts as a valuable reference for individuals looking to enhance their ML approaches and amplify the predictive capabilities of their models.