EnsembleForge: A Comprehensive Framework for Simplified Training and Deployment of Stacked Ensemble Models in Classification Tasks
Abstract
In this work, we introduce EnsembleForge, a versatile framework designed to streamline machine learning experimentation and simplify classification tasks. Leveraging the stacking ensemble method, EnsembleForge offers an intuitive platform built upon the Scikit-learn library. This framework facilitates seamless model implementation and evaluation, supporting both Randomized and Grid Search for hyperparameter optimization. Our experiments with publicly available datasets demonstrate the ease of use and effectiveness of EnsembleForge in experimenting with various algorithms. With its adaptability and innovation, EnsembleForge showcases promising potential to serve as an asset for researchers and practitioners seeking to achieve optimal model performance in their machine learning endeavors.
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