The E-Dimension: Why Machine Learning Doesn't Work Well for Some Problems?

Machine Learning (ML) is closely related to computational statistics which focuses on prediction-making through the use of computers. ML is a modern approach to an old problem: predictive inference. It makes an inference from "feature" space to "outcome/target" space. In order to work properly, an ML algorithm has to discover and model hidden relationships between the feature space and the outcome space and create links between the two. Doing so requires overcoming barriers such as feature noise (randomness of features due to unexplained mechanisms). In this article we argue that "Emergence" is also a barrier for predictive inference.