Improving Predictive Analytics with Ensemble Machine Learning Models
Abstract
Predictive analytics has become a cornerstone in various domains, including finance, healthcare, and marketing, leveraging historical data to forecast future trends. This paper explores the enhancement of predictive analytics using ensemble machine learning models, which combine multiple learning algorithms to improve accuracy and robustness. We review different ensemble methods, including bagging, boosting, and stacking, and their applications in real-world scenarios. The study presents a comparative analysis of these methods using datasets from different sectors, highlighting their strengths and limitations. Our findings demonstrate that ensemble models significantly outperform individual models, providing more reliable predictions and greater generalizability.
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