Examinando por Materia "Logistic Regression"
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Publicación Análisis predictivo de la deserción laboral en BPO : aplicaciones avanzadas de Machine Learning(Universidad EAFIT, 2023) Castelblanco Benítez, Julián; Almonacid Hurtado, Paula MariaÍtem Determining factors in the adoption of corporate social responsibility practices: a sectoral analysis of mexican franchises(Universidad EAFIT, 12/12/2018) María Del Carmen Gaytán Ramírez; Cesario Armando Flores Villanueva; Universidad Autónoma de Nuevo LeónPublicación Machine Learning para la estimación del riesgo de crédito en una cartera de consumo(Universidad EAFIT, 2021) Ossa Giraldo, Wbeimar; Jaramillo Marin, Veronica; Rojas Ormaza, Brayan RicardoFinancial entities, due to their business nature, are inherently exposed to credit risk, for this reason, they are continually searching for new ways to measure the probability of default of clients requesting a loan. This research aims to comparing the precision of a logistic regression model against basic Machine Learning models for estimating credit risk in a consumer loan portfolio, these methodologies are emerging as a key tool for estimating risks due to their flexibility and learning capacity. For this, the Logistic Regression, Random Forest, Support Vector Machine and Multilayer Perceptron models were used, making a comparison in the efficiency of the estimation of the clients that are going to default, and obtaining as a result that the most balanced model at time of evaluation is the Random Forest.