Abstract

GOMES, Cristiano Mauro Assis; LEMOS, Gina C.  and  JELIHOVSCHI, Enio G.. Comparing the Predictive Power of the CART and CTREE algorithms. Aval. psicol. [online]. 2020, vol.19, n.1, pp. 87-96. ISSN 1677-0471.  http://dx.doi.org/10.15689/ap.2020.1901.17737.10.

The CART algorithm has been extensively applied in predictive studies, however, researchers argue that CART produces variable selection bias. This bias is reflected in the preference of CART in selecting predictors with large numbers of cutpoints. Considering this problem, this article compares the CART algorithm to an unbiased algorithm (CTREE), in relation to their predictive power. Both algorithms were applied to the 2011 National Exam of High School Education, which includes many categorical predictors with a large number of categories, which could produce a variable selection bias. A CTREE tree and a CART tree were generated, both with 16 leaves, from a predictive model with 53 predictors and the students' writing essay achievement as the outcome. The CART algorithm yielded a tree with a better outcome prediction. This result suggests that for large data sets, called big data, the CART algorithm might give better results than the CTREE algorithm.

Keywords : algorithms; data mining; large-scale educational assessment; machine learning; National Exam of Upper Secondary Education.

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