Shallow Learning

Shallow learning, encompassing simpler machine learning models like support vector machines and decision trees, focuses on achieving effective classification and prediction with reduced computational complexity and improved interpretability compared to deep learning. Current research emphasizes the development of ensemble methods, active learning strategies for efficient data utilization, and the exploration of shallow learning's strengths in specific applications where data scarcity or explainability is paramount. This renewed interest stems from the need for transparent, efficient models in domains like healthcare, manufacturing, and time series forecasting, where deep learning's "black box" nature poses limitations.