Advancing Predictive Analytics through Hybrid Machine Learning Pipelines for Complex Data Ecosystems
Keywords:
Predictive Analytics, Machine Learning Pipelines, Hybrid Models, Data Ecosystems, Ensemble Learning, Deep Learning, Complex Data, Model Integration, Performance Optimization, Algorithm Integration, Data Science, Multi-Source Data, Scalability, Data Processing, Accuracy EnhancementAbstract
The rapid growth of data in various industries has led to the need for more sophisticated methods of predictive analytics. Hybrid machine learning pipelines, which combine different types of algorithms and data processing frameworks, have emerged as a powerful solution for handling complex data ecosystems. These pipelines enable the integration of diverse machine learning models, allowing for more accurate predictions and better performance. This paper explores how hybrid machine learning pipelines can be used to advance predictive analytics, focusing on the combination of traditional models like regression and decision trees with more complex models such as deep learning and ensemble methods. The goal is to show how such hybrid approaches can enhance predictive capabilities, particularly in large-scale, multi-source data environments.
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Copyright (c) 2021 Michael Morgan Chloe (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
