Machine learning and statistics are closely related fields in terms of methods, but distinct in their principal goal: statistics draws population inferences from a sample, while machine learning finds generalizable predictive patterns.[32] According to Michael I. Jordan, the ideas of machine learning, from methodological principles to theoretical tools, have had a long pre-history in statistics.[33] He also suggested the term data science as a placeholder to call the overall field.[33] Leo Breiman distinguished two statistical modeling paradigms: data model and algorithmic model,[34] wherein "algorithmic model" means more or less the machine learning algorithms like Random forest. Some statisticians have adopted methods from machine learning, leading to a combined field that they call statistical learning.[35]
