Ensemble Approach

Ensemble approaches in machine learning combine predictions from multiple models to improve overall performance, accuracy, and robustness compared to individual models. Current research focuses on applying ensemble methods across diverse fields, utilizing various architectures like convolutional neural networks (CNNs), transformers, and recurrent neural networks (RNNs), often incorporating techniques such as weighted averaging and majority voting. This strategy is proving valuable in applications ranging from medical image analysis and climate forecasting to manufacturing optimization and natural language processing, offering enhanced reliability and reduced reliance on single-model limitations.