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@article{199626,
        author = {Gulab Jangid and Dhruvank Dhamne and Om Bhagat and Meera Sawalkar},
        title = {Ensemble Learning in Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {13936-13941},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=199626},
        abstract = {Ensemble learning constitutes a powerful paradigm within the domain of machine learning that combines the predictive output of multiple base learners to achieve superior generalization performance compared to any individual model. This paper presents a structured survey of ensemble learning methodologies, covering three primary strategies: bagging, boosting, and stacking. We examine foundational algorithms including Random Forest, AdaBoost, Gradient Boosting Machines, XGBoost, and LightGBM, analyzing their theoretical underpinnings, strengths, and computational trade-offs. Additionally, we explore the role of diversity among base learners as a critical determinant of ensemble effectiveness. Experimental comparisons on standard benchmark datasets reveal that ensemble methods consistently outperform single classifiers, with XGBoost and LightGBM demonstrating the most competitive accuracy-efficiency balance. The paper further discusses real-world applications in healthcare, finance, and natural language processing, and identifies open challenges related to interpretability, scalability, and hyperparameter sensitivity. Our findings reinforce ensemble learning as an indispensable toolkit for practitioners across a wide range of predictive modeling tasks.},
        keywords = {},
        month = {April},
        }