Accuracy of Bayes and Logistic Regression Subscale Probabilities for Educational and Certification Tests.

Abstract

In the machine learning literature, it is commonly accepted as fact that as calibration sample sizes increase, Naïve Bayes classifiers initially outperform Logistic Regression classifiers in terms of classification accuracy. Applied to subtests from an on-line final examination and from a highly regarded certification examination, this study shows that the conclusion also applies to the probabilities estimated from short subtests of mental abilities and that small samples can yield excellent accuracy. The calculated Bayes probabilities can be used to provide meaningful examinee feedback regardless of whether the test was originally designed to be unidimensional.