Machine learning methods in prospective studies after an example of financing innovation in Colombia

The purpose of this article is to make a brief introduction to five advanced machine learning prediction methods which may be useful for the development of prospective studies: logistic regression, support vector machines, gradient powered machines, random forests and neural networks. In addition, it is explained what methodology can be carried out to ensure robustness and validate these prediction models. As an example, it is presented how the use of these methods allowed to identify the most important financial variables to predict the development of innovation activities in Colombian SMEs. The results of the use of these methods may allow generating short and medium-term forecasts that serve to facilitate prospective studies with broader methods, such as the construction of scenarios, with the purpose of generating evidence-based proposals as a roadmap for long-term planning and public policy.

• Tipo de recurso: http://purl.org/coar/resource_type/c_6528
• Fecha de publicación: 2020
• Institución: Universidad Pedagógica y Tecnológica de Colombia
• Repositorio: RiUPTC: Repositorio Institucional UPTC
• Idioma: spa
• OAI Identifier: oai:repositorio.uptc.edu.co:001/10331
• Acceso en línea: https://revistas.uptc.edu.co/index.php/investigacion_duitama/article/view/11676; https://repositorio.uptc.edu.co/handle/001/10331
• Palabra clave: logistic regression; support vector machines; gradient powered machines; random forests; neuronal networks; regresión logística; máquinas de vectores de soporte; máquinas de gradiente potencia; bosques aleatorios; redes neuronales
• License: Derechos de autor 2020 REVISTA DE INVESTIGACIÓN, DESARROLLO E INNOVACIÓN