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, i...
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| Format: | Online |
| Language: | spa |
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Universidad Pedagógica y Tecnológica de Colombia
2020
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| Online Access: | https://revistas.uptc.edu.co/index.php/investigacion_duitama/article/view/11676 |
| _version_ | 1801705991647002624 |
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| author | Padilla-Ospina, Ana Milena Medina-Vásquez, Javier Enrique Ospina-Holguín, Javier Humberto |
| author_facet | Padilla-Ospina, Ana Milena Medina-Vásquez, Javier Enrique Ospina-Holguín, Javier Humberto |
| author_sort | Padilla-Ospina, Ana Milena |
| collection | OJS |
| description | 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. |
| format | Online |
| id | oai:oai.revistas.uptc.edu.co:article-11676 |
| institution | Revista de Investigación, Desarrollo e Innovación (RIDI) |
| language | spa |
| publishDate | 2020 |
| publisher | Universidad Pedagógica y Tecnológica de Colombia |
| record_format | ojs |
| spelling | oai:oai.revistas.uptc.edu.co:article-116762021-12-09T23:58:33Z Machine learning methods in prospective studies after an example of financing innovation in Colombia Métodos de aprendizaje automático en los estudios prospectivos desde un ejemplo de la financiación de la innovación en Colombia Padilla-Ospina, Ana Milena Medina-Vásquez, Javier Enrique Ospina-Holguín, Javier Humberto 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 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. El propósito de este artículo es hacer una breve introducción a cinco métodos avanzados de predicción de aprendizaje automático, que pueden ser de utilidad para el desarrollo de estudios prospectivos: la regresión logística, las máquinas de vectores de soporte, las máquinas de gradiente potenciado, los bosques aleatorios y las redes neuronales. Además, se explica qué metodología se puede llevar a cabo para asegurar la robustez y validar dichos modelos de predicción. A manera de ejemplo, se presenta cómo el uso de estos métodos permitió identificar las variables financieras más importantes para predecir el desarrollo de actividades de innovación en pymes colombianas. Los resultados del uso de estos métodos pueden permitir la generación de pronósticos al corto y mediano plazo, que sirvan para la realización de estudios prospectivos con métodos más amplios, como la construcción de escenarios, con el propósito de generar propuestas basadas en la evidencia, como hoja de ruta para la planeación y política pública al largo plazo. Universidad Pedagógica y Tecnológica de Colombia 2020-08-15 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf text/xml https://revistas.uptc.edu.co/index.php/investigacion_duitama/article/view/11676 10.19053/20278306.v11.n1.2020.11676 Revista de Investigación, Desarrollo e Innovación; Vol. 11 No. 1 (2020): Julio-Diciembre; 9-21 Revista de Investigación, Desarrollo e Innovación; Vol. 11 Núm. 1 (2020): Julio-Diciembre; 9-21 2389-9417 2027-8306 spa https://revistas.uptc.edu.co/index.php/investigacion_duitama/article/view/11676/9741 https://revistas.uptc.edu.co/index.php/investigacion_duitama/article/view/11676/11237 Derechos de autor 2020 REVISTA DE INVESTIGACIÓN, DESARROLLO E INNOVACIÓN |
| spellingShingle | 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 Padilla-Ospina, Ana Milena Medina-Vásquez, Javier Enrique Ospina-Holguín, Javier Humberto Machine learning methods in prospective studies after an example of financing innovation in Colombia |
| title | Machine learning methods in prospective studies after an example of financing innovation in Colombia |
| title_alt | Métodos de aprendizaje automático en los estudios prospectivos desde un ejemplo de la financiación de la innovación en Colombia |
| title_full | Machine learning methods in prospective studies after an example of financing innovation in Colombia |
| title_fullStr | Machine learning methods in prospective studies after an example of financing innovation in Colombia |
| title_full_unstemmed | Machine learning methods in prospective studies after an example of financing innovation in Colombia |
| title_short | Machine learning methods in prospective studies after an example of financing innovation in Colombia |
| title_sort | machine learning methods in prospective studies after an example of financing innovation in colombia |
| topic | 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 |
| topic_facet | 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 |
| url | https://revistas.uptc.edu.co/index.php/investigacion_duitama/article/view/11676 |
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