A Brief History of General‐to‐specific Modelling*
回顾了一般到特定建模方法从手动选择到自动化机器学习的发展历程，回应了对其“数据挖掘”的批评，并展示了该方法在非平稳数据中处理多于样本量的变量、选择一致且参数不变的模型的能力。
We review key stages in the development of general‐to‐specific modelling ( Gets ). Selecting a simplified model from a more general specification was initially implemented manually, then through computer programs to its present automated machine learning role to discover a viable empirical model. Throughout, Gets applications faced many criticisms, especially from accusations of ‘data mining’—no longer pejorative—with other criticisms based on misunderstandings of the methodology, all now rebutted. A prior theoretical formulation can be retained unaltered while searching over more variables than the available sample size from non‐stationary data to select congruent, encompassing relations with invariant parameters on valid conditioning variables.