Uso de técnicas de clasificación en un conjunto de datos sobre inclusión financiera: un estudio basado en países de América Latina
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https://doi.org/10.47456/bjpe.v8i1.37019Palabras clave:
Classificación, Inclusión financiera, Procesamiento de datos, América LatinaResumen
La inclusión financiera es importante para reducir la pobreza y proporcionar un crecimiento económico inclusivo, especialmente cuando se comparan grupos con alta desigualdad social. Este artículo utilizó la encuesta Global Financial Inclusion (Global Findex) del Grupo del Banco Mundial para comparar las técnicas de aprendizaje automático en la clasificación de hombres y mujeres por el uso de servicios financieros. Para ello se utilizó el Árbol de Decisión de Clasificadores, -Nearest Neighbors, Naïve Bayes y Random Forest, y se evaluaron las métricas de exactitud, precisión, sensibilidad, f1-score y área bajo la curva Receiver Operating Characteristic (ROC). Se encontró que todas las técnicas (excepto Naïve Bayes) obtuvieron una precisión cercana al 70%, una sensibilidad cercana al 88% y una precisión superior al 72% en la mayoría de los parámetros investigados. En cuanto al área bajo la curva ROC, Random Forest alcanzó 0,77, superando a las demás técnicas en esta evaluación.
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