Evaluating Generalizability

Evaluating the generalizability of machine learning models focuses on assessing their ability to perform well on unseen data, a crucial aspect for reliable real-world applications. Current research investigates this across diverse domains, employing various architectures like convolutional neural networks, graph neural networks, and gradient boosted models, often analyzing performance across different datasets and scenarios to identify sources of poor generalization. Understanding and improving generalizability is vital for building robust and trustworthy AI systems, impacting fields ranging from medical image analysis and autonomous driving to natural language processing and combating misinformation.