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Authors
# Name
1 Nicole Petrica Araujo(n247300@dac.unicamp.br)
2 Maria Luchiari(m205674@dac.unicamp.br,)
3 João Silva(j237409@dac.unicamp.br,)
4 Victor Lima(victorcl@unicamp.br,)
5 Levy Boccato(lboccato@unicamp.br)
6 Romis Attux(attux@unicamp.br)

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Reference
# Reference
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2 Agresti, A. (2013). Categorical Data Analysis. Wiley, 3 edition.
3 Barata, G. (2019). Ainda há muito espaço para mulheres e meninas na ciência e tecnologia. Portal Unicamp. Acesso em: 18 maio 2026.
4 Barocas, S., Hardt, M., and Narayanan, A. (2023). Fairness and Machine Learning: Limitations and Opportunities. MIT Press.
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10 Del Bonifro, F., Gabbrielli, M., Lisanti, G., and Zingaro, S. P. (2020). Student dropout prediction. In Artificial Intelligence in Education, pages 129–140. Springer International Publishing.
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12 Feldman, M., Friedler, S. A., Moeller, J., Scheidegger, C., and Venkatasubramanian, S. (2015). Certifying and removing disparate impact. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 259–268. ACM.
13 Hardt, M., Price, E., and Srebro, N. (2016). Equality of opportunity in supervised learning. In Advances in Neural Information Processing Systems, volume 29, pages 3315–3323.
14 Heidari, S., Babor, T. F., De Castro, P., Tort, S., and Curno, M. (2016). Sex and gender equity in research: Rationale for the sager guidelines and recommended use. Research Integrity and Peer Review, 1(2).
15 Kakkar, S., Jaya Lakshmi, A., Durai C, A. D., Ahmad, W., Ayoobkhan, M. U. A., Veeramanikandan, P., Reddy, P. N., Dwivedi, V. K., and Rajaram, A. (2024). Enhancing energy efficiency and classification modeling through a combined approach of LightGBM and stratified KFold cross-validation. Electric Power Components and Systems, pages 1–19.
16 Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.-Y. (2017). LightGBM: A highly efficient gradient boosting decision tree. In Advances in Neural Information Processing Systems, volume 30.
17 Kemper, L., Vorhoff, G., and Wigger, B. U. (2019). Early identification of college dropouts using machine-learning: Conceptual considerations and an empirical example. IZA Research Report 89, IZA Institute of Labor Economics.
18 Kleinberg, J., Mullainathan, S., and Raghavan, M. (2017). Inherent trade-offs in the fair determination of risk scores. In Proceedings of the 8th Innovations in Theoretical Computer Science Conference, volume 67 of LIPIcs, pages 43:1–43:23. Schloss Dagstuhl–Leibniz-Zentrum fuer Informatik.
19 Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., and Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys, 54(6):1–35.
20 Owusu-Adjei, M., Ben Hayfron-Acquah, J., Frimpong, T., and Abdul-Salaam, G. (2023). Imbalanced class distribution and performance evaluation metrics: A systematic review of prediction accuracy for determining model performance in healthcare systems. PLOS Digital Health, 2(11):e0000290.
21 Sharpe, D. (2015). Your chi-square test is statistically significant: Now what? Practical Assessment, Research, and Evaluation, 20(8):1–10.
22 Silva, F. C. d., Cabral, T. L. d. O., and Pacheco, A. S. V. (2020). Evasão ou permanência? modelos preditivos para a gestão do ensino superior. Education Policy Analysis Archives, 28(149):1–32.
23 Silva Filho, R. L. L. e., Motejunas, P. R., Hipólito, O., and Lobo, M. B. d. C. M. (2007). A evasão no ensino superior brasileiro. Cadernos de Pesquisa, 37(132):641–659.
24 Universidade Estadual de Campinas (2004). Deliberação CONSU-A-012/2004, de 25/05/2004: Estabelece o Programa de Ação Afirmativa para Inclusão Social na UNICAMP. Conselho Universitário.
25 Universidade Estadual de Campinas (2017). Deliberação CONSU-A-032/2017, de 21/11/2017: Dispõe sobre os sistemas de ingresso aos cursos de Graduação da Unicamp. Conselho Universitário.
26 Wasserstein, R. L. and Lazar, N. A. (2016). The asa statement on p-values: Context, process, and purpose. The American Statistician, 70(2):129–133.