Optimizing Decision Trees: Exploring Pruning Techniques and the Impact of Ensemble Classifiers

The advent of machine learning has revolutionized decision-making processes by enabling machines to make rapid decisions beyond human capability. Among the numerous applications, classification stands out as a pivotal field where models ascertain the category or class of an object, often guided by decision trees. However, managing complex and voluminous datasets poses challenges, prompting exploration into techniques like ensembling and pruning decision trees to enhance performance and expedite classification. While the individual merits of ensembling and pruning are acknowledged, their comparative effectiveness remains ambiguous, particularly in resource-constrained environments. This paper investigates these methodologies through empirical analysis, delineating their limitations and juxtaposing two approaches tailored for low-capability computing devices: pruning base models before ensemble construction and relying solely on ensembling without pruning. Moreover, it sheds light on the criteria governing the selection of pruning and ensemble algorithms and elucidates their practical utility through experimentation. This study contributes insights into optimizing classification models for resource-constrained settings, aiding practitioners in informed decision-making regarding model selection and deployment.