25 Years of Conventional Evaluation of Data Analysis Proves Worthless in Practice
Uppsala University (09/03/08)
For the past 25 years, two methods have been used to evaluate computer-based methods for classifying patient samples, but Swedish researchers at Uppsala University have found that this methodology is worthless when applied to practical problems. These methods are the basis for many technical applications, such as recognizing human speech, images, and fingerprints, and are now being used in new fields such as health care. However, to evaluate the performance of a classification model, a number of trial examples that were never used in the design of the model are needed. Unfortunately, there are seldom tens of thousands of test samples available for this type of evaluation, often because the samples are too rare or expensive to collect to use on an evaluation. Numerous methods have been proposed to solve this problem and since the 1980s two methods have dominated the field--cross validation and resampling/bootstrapping. The Uppsala researchers used both theory and computer simulations to show that those methods are worthless in practice when the total number of examples is small in relation to the natural variation that exists among different observations. What constitutes a small number depends on the problem being studied. The researchers say it is essentially impossible to determine whether the number of examples used is sufficient. "Our main conclusion is that this methodology cannot be depended on at all, and that it therefore needs to be immediately replaced by Bayesian methods, for example, which can deliver reliable measures of the uncertainty that exists," says Uppsala University professor Mats Gustafsson, who co-directed the study with professor Anders Isaksson. "Only then will multivariate analyses be in any position to be adopted in such critical applications as health care."
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