Batch Learning

Batch learning, a machine learning paradigm where models are trained on complete datasets before deployment, is undergoing renewed scrutiny, particularly in scenarios with streaming data or delayed labels. Current research focuses on comparing batch methods against online or incremental approaches, investigating their performance and efficiency across various applications like fraud detection, reinforcement learning, and text evaluation, often employing models such as XGBoost, Adaptive Random Forests, and large language models. This renewed interest stems from the need for robust and interpretable models in real-world applications where data arrives sequentially or labels are unavailable immediately, highlighting the ongoing importance of understanding the trade-offs between batch and online learning strategies.