Application of machine learning for predictions of consecutive dependent data of type {[(a, b)->c]->d}

Aplicación de machine learning para predicciones de datos dependientes consecutivos de tipo {[(a, b)->c]->d}

Authors

Keywords:

algorithms, datasets, decision trees, Python, prediction, scikit-learn, linear regression (en).

Keywords:

algoritmos, datasets, árboles de decisión, Python, scikit-learn, regresión lineal (es).

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Abstract (en)

Objective: Machine learning techniques have emerged in response to the desire for automatic pattern detection withindatasets in fields such as statistics, mathematics, and data analytics. They allow for the extraction of relevant informationfrom datasets of significantly large volumes, providing the possibility of making predictions. This paper presents an application focused on decision trees, linear regression, and random forest regression algorithms to predict final data fromconsecutive dependent data of type {[(a, b) → c] → D}.

Methodology: The study adopts a quantitative research design, which takes as input datasets based on interval data. It utilizes a correlational research model by implementing Python and its Scikit-Learn library, which includes various algorithms for prediction. Specifically, we compare the application of decision trees, linear regression, and random forest regression on the same set of datasets, but with a characteristic of dependency between them.

Results: Upon application of the proposed model, it yields an estimated prediction score, which indicates the accuracy of the model concerning the data provided.

Conclusions: The application of a complex algorithm does not inherently guarantee a higher rate of accuracy. Conversely, configuring the model correctly, training multiple trees, or adjusting parameter values can significantly enhance the obtained results

Abstract (es)

Objetivo: Las técnicas de Machine Learning surgen como una respuesta al deseo de detectar automáticamente patrones en un conjunto de datos (datasets) en campos como la estadística, la matemática y la analítica de datos, permitiendo extraer información relevante de datasets de volúmenes significativamente grandes y realizar predicciones. Éste artículo presenta una aplicación enfocada en los algoritmos de árboles de decisión, regresión lineal y regresión aleatoria de tipo bosque para predecir un dato final a partir de datos dependientes consecutivos de tipo {[(a, b) → c] → D}.

Metodología: Se parte de un diseño de investigación cuantitativo, que toma como insumo unos datasets basados en datos de intervalo, establecidos en un modelo de investigación correlacional al aplicar Python y su librería Scikit-learn. Esta biblioteca incluye diferentes algoritmos que pueden ser utilizados para realizar predicciones. En este caso, se compara la aplicación de árboles de decisión, regresión lineal y regresión aleatoria de tipo bosque sobre un mismo grupo de datasets, pero que tienen una característica de dependencia entre ellos.

Resultados: Cuando se aplica el modelo propuesto, este genera un puntaje estimado de la predicción, el cual indica la precisión del modelo respecto a los datos entregados.

Conclusiones: La aplicación de un algoritmo complejo no garantiza un mayor índice de precisión; por el contrario, configurar de manera correcta el modelo, entrenando múltiples árboles o cambiando los valores de los parámetros mejora en gran medida los resultados obtenidos

Author Biographies

Diego Alexander Quevedo Piratova, Fundación Universitaria Compensar

Magister in Educational Technology, Master's in Innovative Media for Education. Graduate in Technological Design, systems engineering student. Research professor at the School of Engineering of the Compensar University Foundation. Bogotá, Colombia

Jhon Uberney Londoño Villalba, Corporación Unificada Nacional de Educación Superior

Master in Educational Technology Management, Specialist in Virtual Learning Environments. Graduated in Technological Design, systems engineering student. Research professor of the AXON research group attached to the systems engineering program of the engineering school of the National Unified Corporation for Higher Education CUN. Bogotá Colombia.

Arnaldo Andres Gonzalez Gomez, Corporación Unificada Nacional de Educación Superior

Electronic Engineer graduated from the Francisco José de Caldas District University, specializing in Data Analytics. Research professor of the AXON research group assigned to the systems engineering program of the engineering school of the National Unified Corporation for Higher Education CUN . Bogotá Colombia.

References

Bell, J. (2015). Machine learning Hands-On for Developers and Technical Professionals. Indiana: Wiley.

Breiman, L. (2001). Random Forest. California. University of California. https://www.stat.berkeley.edu/~breiman/randomforest2001.pdf

Cardona, D., Rivera, M., González, J., & Cárdenas, E. (2014). Estimación y predicción con el modelo de regresión cúbica aplicado a un problema de salud. Ingenieria Solidaria, 10(17). https://doi.org/10.16925/in.v9i17.828

Díaz Martínez, Z., Fernández Menéndez, J., & Segovia Vargas, J. (2004). Sistemas de inducción de reglas y árboles de decisión aplicados a la predicción de insolvencias en empresas aseguradoras. Departamento de Economía Financiera y Contabilidad / Departamento de Organización de Empresas. Madrid: Universidad Complutense de Madrid. https://eprints.ucm.es/id/eprint/6833/

Feurer, M., & Hutter, F. (2019). Hyperparameter Optimization. In Automated Machine Learning (pp. 3-33). Springer. https://doi.org/10.1007/978-3-030-05318-5

García ruiz de León, M., Mira McWilliams, J. M., & Ahrazem Dfuf, I. (2018). Análisis de sensibilidad mediante Random Forest. Madrid: Universidad Politécnica de Madrid.

Hinestroza Ramírez, D., & Cárdenas, J. M. (2018). El Machine Learning a través de los tiempos, y los aportes a la humanidad. Pereira: Universidad Libre.

Maisueche Cuadrado, A. (2019). Utilización del Machine Learning en la industria 4.0. Valladolid: Universidad de Valladolid. Escuela de Ingenierías Industriales. http://uvadoc.uva.es/handle/10324/37908

Maisueche Cuadrado, A. (2019). Montero Granados, R. (2016). Modelos de regresión lineal múltiple. Granada, España: Universidad de Granada. http://www.ugr.es/~montero/matematicas/regresion_lineal.pdf

Segura Cardona, A. M. (2012). Aplicación de árboles de decisión en la salud pública (Implementation of decision trees in public health) (Aplicação de árvores de decisão em saúde pública). Revista CES salud pública, 3(1), 94-103. http://dx.doi.org/10.21615/2140

Song, Y.-Y.,& Ying, L. (2015). Decision tree methods: applications for classification and prediction. Shanghai Arch Psychiatry, 27(2), 130-135. https://doi.org/10.11919/j.issn.1002-0829.215044

How to Cite

APA

Quevedo Piratova, D. A., Londoño Villalba, J. U., and Gonzalez Gomez, A. A. (2024). Application of machine learning for predictions of consecutive dependent data of type {[(a, b)->c]->d}. Tecnura, 28(79), 66–86. https://doi.org/10.14483/22487638.22094

ACM

[1]
Quevedo Piratova, D.A. et al. 2024. Application of machine learning for predictions of consecutive dependent data of type {[(a, b)->c]->d}. Tecnura. 28, 79 (Oct. 2024), 66–86. DOI:https://doi.org/10.14483/22487638.22094.

ACS

(1)
Quevedo Piratova, D. A.; Londoño Villalba, J. U.; Gonzalez Gomez, A. A. Application of machine learning for predictions of consecutive dependent data of type {[(a, b)->c]->d}. Tecnura 2024, 28, 66-86.

ABNT

QUEVEDO PIRATOVA, Diego Alexander; LONDOÑO VILLALBA, Jhon Uberney; GONZALEZ GOMEZ, Arnaldo Andres. Application of machine learning for predictions of consecutive dependent data of type {[(a, b)->c]->d}. Tecnura, [S. l.], v. 28, n. 79, p. 66–86, 2024. DOI: 10.14483/22487638.22094. Disponível em: https://revistas.udistrital.edu.co/index.php/Tecnura/article/view/22094. Acesso em: 21 nov. 2024.

Chicago

Quevedo Piratova, Diego Alexander, Jhon Uberney Londoño Villalba, and Arnaldo Andres Gonzalez Gomez. 2024. “Application of machine learning for predictions of consecutive dependent data of type {[(a, b)->c]->d}”. Tecnura 28 (79):66-86. https://doi.org/10.14483/22487638.22094.

Harvard

Quevedo Piratova, D. A., Londoño Villalba, J. U. and Gonzalez Gomez, A. A. (2024) “Application of machine learning for predictions of consecutive dependent data of type {[(a, b)->c]->d}”, Tecnura, 28(79), pp. 66–86. doi: 10.14483/22487638.22094.

IEEE

[1]
D. A. Quevedo Piratova, J. U. Londoño Villalba, and A. A. Gonzalez Gomez, “Application of machine learning for predictions of consecutive dependent data of type {[(a, b)->c]->d}”, Tecnura, vol. 28, no. 79, pp. 66–86, Oct. 2024.

MLA

Quevedo Piratova, Diego Alexander, et al. “Application of machine learning for predictions of consecutive dependent data of type {[(a, b)->c]->d}”. Tecnura, vol. 28, no. 79, Oct. 2024, pp. 66-86, doi:10.14483/22487638.22094.

Turabian

Quevedo Piratova, Diego Alexander, Jhon Uberney Londoño Villalba, and Arnaldo Andres Gonzalez Gomez. “Application of machine learning for predictions of consecutive dependent data of type {[(a, b)->c]->d}”. Tecnura 28, no. 79 (October 27, 2024): 66–86. Accessed November 21, 2024. https://revistas.udistrital.edu.co/index.php/Tecnura/article/view/22094.

Vancouver

1.
Quevedo Piratova DA, Londoño Villalba JU, Gonzalez Gomez AA. Application of machine learning for predictions of consecutive dependent data of type {[(a, b)->c]->d}. Tecnura [Internet]. 2024 Oct. 27 [cited 2024 Nov. 21];28(79):66-8. Available from: https://revistas.udistrital.edu.co/index.php/Tecnura/article/view/22094

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