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Authors
# Name
1 Lucas Praxedes(lucas.praxedes@alunos.ufersa.edu.br)
2 Lenardo Chaves(lenardo@ufersa.edu.br)
3 Breno da Costa(breno.duarte@nees.ufal.br)
4 Nicholas da Cruz(nicholas.cruz@nees.ufal.br)
5 Rafael Silva(rafael.amorim@nees.ufal.br)
6 Bruno Pimentel(bruno.pimentel@nees.ufal.br)

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Reference
# Reference
1 BREIMAN, L. Random Forests. Machine Learning, v. 45, n. 1, p. 5–32, 2001.
2 COVER, T. M.; THOMAS, J. A. Elements of Information Theory. 2. ed. Hoboken, NJ: Wiley-Interscience, 2006.
3 HOERL, A. E.; KENNARD, R. W. Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, v. 12, n. 1, p. 55–67, 1970.
4 FUNDAÇÃO ROBERTO MARINHO; INSPER. Consequências da Violação do Direito à Educação. Rio de Janeiro: Fundação Roberto Marinho, 2021.
5 KOHAVI, R. A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence - Volume 2. Montreal, Canada: Morgan Kaufmann Publishers Inc., 1995. p. 1137–1143.
6 KOTSIANTIS, S. B. Supervised Machine Learning: A Review of Classification Techniques. Informatica, v. 31, n. 3, p. 249–268, 2007.
7 SHAO, L. et al. Machine Learning Methods for Course Enrollment Prediction. Strategic Enrollment Management Quarterly
8 SOUZA, A. M. et al. Beyond scores: A machine learning approach to comparing educational system effectiveness. PLOS ONE, v. 18, n. 10, p. e0289260, 2023.
9 LUNDBERG, S. M.; LEE, S.-I. A Unified Approach to Interpreting Model Predictions. In: Advances in Neural Information Processing Systems 30 (NIPS 2017), 2017.