[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119232-es":3,"doc-seo-119232-110":31,"detail-sidebar-cat-0-es-110":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},119232,962084928904,"Asher","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",41,"Investigación e Informes","INVESTIGACIÓN - Aplicación de machine learning para predicciones de datos dependientes consecutivos de tipo {[(a, b) → c] → d}","Las técnicas de machine learning surgen para detectar automáticamente patrones en conjuntos de datos en áreas como estadística, matemáticas y analítica de datos, posibilitando la extracción de información relevante y la realización de predicciones. El trabajo presenta una aplicación que emplea árboles de decisión, regresión lineal y regresión aleatoria tipo bosque para predecir un dato final a partir de datos dependientes consecutivos. El enfoque es cuantitativo y correlacional, con implementación en Python y Scikit-Learn, comparando modelos sobre conjuntos con dependencia; los resultados evalúan la precisión mediante un puntaje de predicción estimado.","INVESTIGACIÓN  \nApplication of machine learning for predictions of consecutive  \ndependent data of type {[(a, b) → c] →− d}  \nAplicación de machine learning para predicciones de datos dependientes  \nconsecutivos de tipo {[(a, b) → c] →− d}  \nDiego Alexander Quevedo Piratova1, Jhon Uberney Londoño Villalba2, Arnaldo Andres  \nGonzalez Gomez3  \nFecha de Recepción: 30 de enero de 2023 Fecha de Aceptación: 23 de abril de 2024  \nCómo citar: Quevedo-Piratova., D.A. , Villalba-Londoño., J.U. y Gonzalez-Gomez., A.A (2022) . 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](https://doi.org/10.14483/22487638.22094)  \nABSTRACT  \nObjective: Machine learning techniques have emerged in response to the desire for automatic pattern detection within datasets in fields such as statistics, mathematics, and data analytics. They allow for the extraction of relevant information from 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 from consecutive dependent data of type {[(a, b) → c] → D} .  \nMethodology: 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.  \nResults: Upon application of the proposed model, it yields an estimated prediction score, which indicates the accuracy of the model concerning the data provided.  \n1Magister in Educational Technology, Magister in Innovative Media for Education. 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. [Email: diego_quevedo@cun.edu.co](Email: diego_quevedo@cun.edu.co)  \n2Master 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. [Email: jhon.londono@cun.edu.co](Email: jhon.londono@cun.edu.co)  \n3Electronic 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.  \nEmail: [arnaldo_gonzalez@cun.edu.co](arnaldo_gonzalez@cun.edu.co)  \nTecnura • p-ISSN: 0123-921X • e-ISSN: 2248-7638 • Volumen 28 Número 79 • Enero-Marzo de 2024 • pp. 66-86  \n[66]  \nApplication of machine learning for predictions of consecutive dependent data of type {[(a, b) → c] →− d} Quevedo Piratova Diego Alexander, Londoño Villalba Jhon Uberney, Gonzalez Gomez Arnaldo Andres  \n\n| 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\u003Cbr>Financing: Unified National Corporation for Higher Education (CUN) .\u003Cbr>Keywords: algorithms, datasets, decision trees, Python, prediction, Scikit-Learn, linear regression |\n| --- |\n| RESUMEN\u003Cbr>Objetivo: Las técnicas de Machine Learning surgen como una respuesta al deseo de detectar automáticamente p","cbCaiq46qsswk5sC","https://ap.wps.com/l/cbCaiq46qsswk5sC","pdf",4047661,8,1,21,"Spanish","es",110,"# Resumen (Abstract)\n# Introducción\n# Metodología\n# Resultados\n# Conclusiones","[{\"question\":\"¿Cuál es el objetivo del artículo sobre machine learning?\",\"answer\":\"Aplicar técnicas de machine learning para realizar predicciones a partir de datos dependientes consecutivos, logrando estimar un dato final mediante modelos supervisados.\"},{\"question\":\"¿Qué algoritmos se comparan en el estudio?\",\"answer\":\"Se comparan árboles de decisión, regresión lineal y regresión aleatoria tipo bosque aplicados sobre los mismos conjuntos de datos con dependencia entre ellos.\"},{\"question\":\"¿Cómo se implementa la metodología y con qué herramientas?\",\"answer\":\"La investigación usa un diseño cuantitativo correlacional e implementa los modelos con Python y la librería Scikit-Learn para ejecutar y comparar algoritmos de predicción.\"}]","INVESTIGACIÓN - 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