Optimization Strategies for Big Data Prediction Models Based on Ensemble Learning Techniques

Abstract

This paper aims to explore the optimization strategies for prediction models in a big data environment using ensemble learning
techniques. By thoroughly analyzing the multiple algorithm fusion mechanisms within the ensemble learning framework, it proposes an
optimization path for the efficient processing and accurate prediction of large-scale datasets. In multiple dimensions such as data preprocessing, feature engineering, model selection, and fusion, this paper systematically elaborates on how to utilize ensemble learning methods
to enhance the robustness and accuracy of prediction models, ensuring stable and reliable prediction performance in a complex and variable data environment. By constructing an optimized ensemble prediction system, this paper provides theoretical basis and practical guidance for the field of big data prediction.