Examinando por Materia "SHAP"
Mostrando 1 - 4 de 4
Resultados por página
Opciones de ordenación
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 AlbertoPublicación Estimación del efecto de las variables ambientales en la producción agrícola exportable en Antioquia usando modelos de ML(Universidad EAFIT, 2025) Páez Bermúdez, Johan Stiven; García Vargas, Johan FelipeÍtem Metodología analítica para la estrategia de precios en pymes : una aplicación en el sector automotriz(Universidad EAFIT, 2024) Álvarez Restrepo, Victoria; Saldarriaga Aristizábal, Pablo AndrésÍtem Segmentación de los flujos migratorios en Colombia : identificación de subgrupos y características comunes(Universidad EAFIT, 2024) Aguirre Marín, Cindy Vanessa; Martínez Vargas, Juan David; Sepúlveda Cano, Lina MaríaThe increase in global migration has intensified migratory flows, emerging as a relevant phenomenon for global, regional, and national policies. In Colombia, since 2015, Venezuelan migration has sparked interest in migratory flows. This study analyzes migratory flows to Colombia in 2023 using Machine Learning techniques. K-Means was applied in order to segment data from Migration Colombia, while UMAP was used to reduce the dimensionality of the data itself. The results reveal four main clusters, defined by the region of origin, reason for travel, host region, and month of arrival. Most flows correspond to tourists, suggesting that the data from official migration points primarily reflect tourist movements and not necessarily other types of migration. Machine Learning techniques proved effective in uncovering complex patterns in categorical data, and interpretation using SmartExplainer by SHAPash facilitated the understanding of these patterns. This study not only adequately segmented migratory flows but also provided interpretative tools for future analyses of categorical data.