Machine Learning vs Deep Learning: The Generalization Problem

arXiv:2403.01621v1 Announce Type: new
Abstract: The capacity to generalize beyond the range of training data is a pivotal challenge, often synonymous with a model's utility and robustness. This study investigates the comparative abilities of traditional machine learning (ML) models and deep learning (DL) algorithms in terms of extrapolation -- a more challenging aspect of generalization because it requires the model to make inferences about data points that lie outside the domain it has been trained on. We present an empirical …

abstract algorithms arxiv beyond capacity challenge cs.ai cs.lg data deep learning machine machine learning pivotal robustness study terms traditional machine learning training training data type utility