Forensics: Assessing Model Goodness: A Machine Learning View

It is not unusual that efforts to validate a statistical model exceed those used to build the model. Multiple techniques are used to validate, compare and contrast among competing statistical models: Some are concerned with a model’s ability to predict new data while others are concerned with model descriptiveness of the data. Without claiming to provide a comprehensive view of the landscape, in this paper we will touch on both aspects of model validation. There is much more to the subject and the reader is referred to any of the many classical statistical texts including the revised two volumes of Bickel and Docksum (2016), the one by Hastie, Tibshirani, and Friedman [The Elements of Statistical Learning: Data Mining, Inference, and Predication, 2nd edn. (Springer, 2009)], and several others listed in the bibliography.