Hybrid Machine Learning Models for Enhanced Prediction and Classification Accuracy

Rohitash Chandra

Hybrid Machine Learning Models for Enhanced Prediction and Classification Accuracy

Author(s)	Rohitash Chandra
Country	Australia
Abstract	The rapid expansion of data-driven applications has increased the demand for machine learning models capable of delivering accurate, reliable, and adaptable predictions across complex decision-making environments. Conventional single-algorithm approaches may encounter limitations when datasets contain nonlinear relationships, high-dimensional features, class imbalance, noise, or heterogeneous data structures. This study examines the potential of hybrid machine learning models that integrate complementary learning techniques to improve prediction and classification accuracy. The proposed approach combines ensemble learning, feature optimization, and hybrid predictive architectures to exploit the strengths of different algorithms while reducing their individual limitations. A quantitative comparative research framework is proposed for evaluating conventional and hybrid models using accuracy, precision, recall, F1-score, and predictive error. The analytical framework considers representative algorithms including logistic regression, decision trees, support vector machines, random forests, gradient boosting, and hybrid ensemble configurations. The study demonstrates, through an illustrative benchmark framework, how model integration can improve robustness, generalization, and classification performance. The findings suggest that hybridization is particularly valuable when data relationships are nonlinear and heterogeneous. The study contributes an integrated perspective for applying hybrid machine learning to multidisciplinary intelligent systems and provides practical directions for future research involving explainability, adaptive learning, multimodal data, and real-time decision support.
Keywords	Hybrid Machine Learning, Prediction Accuracy, Classification, Ensemble Learning, Data Analytics, Intelligent Decision Making, Predictive Modeling, Artificial Intelligence.
Field	Engineering
Published In	Volume 1, Issue 1, January-February 2019
Published On	2019-02-01

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American International Journal of Advanced Research and Development

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Hybrid Machine Learning Models for Enhanced Prediction and Classification Accuracy

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