Examinando por Materia "XGBoost"
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Ítem Análisis de riesgo de impago en el sector financiero : enfoque en tarjetas de crédito(Universidad EAFIT, 2024) Herrera Olivares, Maher Stehisy; Moreno Reyes, Nicolás AlbertoÍtem Clasificación de créditos de libranza negociados en el mercado secundario colombiano, aplicando técnicas de aprendizaje supervisado(Universidad EAFIT, 2024) Gómez Betancur , Juan Camilo; Moreno Reyes, Nicolás AlbertoCredit risk, exacerbated by events such as the 2008 financial crisis, remains a concern for both banking and non-banking entities. This study addresses the need to improve the classification of payroll loans in Colombia using both traditional and machine learning techniques. It highlights the superior effectiveness of supervised learning algorithms in credit risk classification, with the ultimate goal of developing a model capable of identifying loans with a higher probability of default. This would optimize the acquisition of payroll loans and strengthen investment portfolio.Ítem Determinantes del riesgo de incumplimiento en créditos educativos : un análisis para Colombia(Universidad EAFIT, 2020) Granda Rodríguez, Manuela Andrea; Posso Suárez, Christian ManuelThis document uses non-parametric Machine Learning methodologies, in particular the XGBoost algorithm, to predict the risk of non-compliance with educational credits in Colombia offered by ICETEX between 2015 and 2018. The interest variable is the risk of default in student credits and is used as variable determinants associated with the socioeconomic level of students, as well as school information and academic achievement for each student. The main results show that socioeconomic variables with very good default predictors, in particular variables such as parent education and scores on critical reading tests are strong predictors. The results found contribute to economic and social policy decisions on the design of methods for higher education coverage through meritorious credits with public and private funds.Ítem Hacia un modelo predictivo para identificar aspectos clave en la rotación de empleados(Universidad EAFIT, 2023) Cárdenas López, Paula Andrea; Tabares Betancur, Marta SilviaÍtem Predictive Model for the Detection of Natural Gas Customers Who Consume the Service Without Being Billed(EAFIT, 2022) Jaramillo Zapata, Diego Andrés; Tabares Betancur, Marta Silvia; Tabares Betancur, Marta Silvia