Ensemble Accuracy

Ensemble methods aim to improve the accuracy and robustness of machine learning models by combining predictions from multiple individual models. Current research focuses on enhancing ensemble efficiency through techniques like optimizing the diversity of base models, leveraging low-loss subspaces for faster inference, and developing efficient training strategies such as knowledge transfer and asynchronous hyperparameter optimization. These advancements are significant because they address the computational cost often associated with ensemble methods, making them more practical for large-scale applications and diverse data types, ultimately leading to more accurate and reliable predictions.