Module 43: Data Mining for Classification and Regression
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Data mining methods for classification and regression are becoming increasingly popular in various scientific fields. However, these methods have not been explored much in educational measurement. This module first provides a review, which should be accessible to a wide audience in education measurement, of some of these methods. The module then demonstrates using three real-data examples that these methods may lead to an improvement over traditionally used methods such as linear and logistic regression in educational measurement.
Keywords: bagging, boosting, classification and regression tree, CART, cross-validation error, data mining, predicted values, random forests, supervised learning, test error, TIMSS