Examinando por Materia "Bosques aleatorios"
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Ítem Comparación entre el método tradicional y algunos basados en inteligencia artificial para el estudio del riesgo crediticio en instituciones financieras colombianas(Universidad EAFIT, 2018) Arango Correa, Diana Marcela; Colmenares Colmenares, Laura Juliana; Rave Contreras, Isabel Cristina; Martínez Negrete, Milton AlfonsoArtificial intelligence models are an open problem for application in various fields of science and search of variable relationships especially when the distribution of events doesn’t depend on a linear function; through this work we want to compare the traditional method most used for credit behavior monitoring with advanced models of artificial intelligence -- The guides that exist in Colombia for management of credit risk are given by the Financial Superintendence of Colombia, international standards such as Basel II, Basel III and Solvency are based on the logistic regression and the discriminant analysis, models used by financial institutions in Colombia to measure credit behavior, thus we carried out an investigation to explore the utility of new models -- This paper addresses one of the traditional methods used in financial institutions, that is, logistic regression, and compares it with alternative methods such as neural networks and random forests -- From the literature review and using a database provided by a banking entity, the dependent variables and the response variable are selected, the logistic regression models, random forests and neural networks are calibrated in the Microsoft Azure Machine Learning application and they are compared to each other with indicators of precision and accuracy such as ROC (from receiver operating characteristic) curve and confusion matrix, obtaining for the models of artificial intelligence, results as good as the traditional one; so they can be used by the financial sector as alternate and / or complementary methods in the analysis of credit riskÍtem Estimación de precio de oferta para una planta hidroeléctrica de baja regulación en la bolsa de energía(Universidad EAFIT, 2021) Mosquera Galvis, Liceth Cristina; Quintero Montoya,Olga LuciaÍtem Predicción del precio del oro en el mercado spot y el tipo de cambio USD–COP para la optimización del rango de cobertura en derivados de las compañías exportadoras del sector minero(Universidad EAFIT, 2024) Gallego Panesso, Cristian Alexander; Almonacid Hurtado, Paula MaríaThis study addresses the implementation of various time series regression and machine learning models, such as: ARIMA, ARIMAX, SARIMA and Random Forests with the objective of accurately predicting the price of gold in the spot market and the USD–COP exchange rate. Precision in these predictions is crucial for export companies in the mining sector, as it allows them to establish optimal coverage ranges in the use of financial derivatives. Throughout the study, different machine learning algorithms were evaluated and compared, selecting those that provided the most accurate and consistent results. The findings offer a valuable tool for financial risk management and strategic decision making in the context of gold price volatility and exchange rate fluctuations. At the end of the study, it is indicated that the ARIMAX Rolling Forecast model applied in a parameterization (1,1,0) was the most accurate and consistent model over time for the price forecasts of both assets.Ítem RF-kNN: A Novel Ensemble Method for Improved Classification tasks(Universidad EAFIT, 2023) Muñoz Mercado, José Jorge; Almonacid Hurtado, Paula María; López Aguirre, Esteban