title: Classification in karyometry: performance testing and prediction error.
abstract: Classification plays a central role in quantitative histopathology. Success is expressed in terms of the accuracy of prediction for the classification of future data points and an estimate of the prediction error. The prediction error is affected by the chosen procedure, e.g., the use of a training set of data points, a validation set, an independent test set, the sample size and the learning curve of the classification algorithm. For small samples procedures such as the "jackknife," the "leave one out" and the "bootstrap" are recommended in order to arrive at an unbiased estimate of the true prediction error. All of the procedures rest on the assumption that the data set used to derive a classification rule is representative for the diagnostic categories involved. It is this assumption that in quantitative histopathology has to be carefully verified before a clinically generally valid classification procedure can be claimed.
authors: College of Optical Sciences and Arizona Cancer Center, University of Arizona, Tucson, Arizona 85724-5024, USA. hubertbartels@msn.com
authors: Peter H
authors: PH
authors: Bartels
authors: College of Optical Sciences and Arizona Cancer Center, University of Arizona, Tucson, Arizona 85724-5024, USA.
authors: Hubert G
authors: HG
authors: Bartels
mesh_terms: D000465:Algorithms; D005103:Expert Systems; D006652:Histological Techniques / Q000145:classification* / Q000592:standards*; D006801:Humans; D007620:Karyometry / Q000145:classification* / Q000592:standards*; D015233:Models, Statistical; D010338:Pathology, Clinical / Q000145:classification* / Q000592:standards*; D011237:Predictive Value of Tests; D011786:Quality Control