Satisfacción del turista usando factores motivacionales: comparación de modelos de aprendizaje estadístico

El nivel de satisfacción de un turista con el destino visitado y su intención de volver a visitarlo se asumen como dependientes de su experiencia previa con el lugar. Para observar esta perspectiva relacional, se utilizó un conjunto de datos de 386 turistas que visitaron la ciudad de Medellín (Colo...

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Autores:: Vanegas , Juan Gabriel
Muñetón Santa , Guberney

Tipo de recurso:: Article of journal

Fecha de publicación:: 2023

Institución:: Universidad Externado de Colombia

Repositorio:: Biblioteca Digital Universidad Externado de Colombia

Idioma:: spa

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repository_id_str
dc.title.spa.fl_str_mv	Satisfacción del turista usando factores motivacionales: comparación de modelos de aprendizaje estadístico
dc.title.translated.eng.fl_str_mv	Tourist Satisfaction Using Motivational Factors: Comparison of Statistical Learning Models
title	Satisfacción del turista usando factores motivacionales: comparación de modelos de aprendizaje estadístico
spellingShingle	Satisfacción del turista usando factores motivacionales: comparación de modelos de aprendizaje estadístico tourist satisfaction, tourist motivations, supervised learning, machine learning algorithms, Support Vector Machines, Medellín satisfacción del turista, motivaciones del turista, aprendizaje supervisado, algoritmos de aprendizaje estadístico, máquinas de soporte vectorial, Medellín
title_short	Satisfacción del turista usando factores motivacionales: comparación de modelos de aprendizaje estadístico
title_full	Satisfacción del turista usando factores motivacionales: comparación de modelos de aprendizaje estadístico
title_fullStr	Satisfacción del turista usando factores motivacionales: comparación de modelos de aprendizaje estadístico
title_full_unstemmed	Satisfacción del turista usando factores motivacionales: comparación de modelos de aprendizaje estadístico
title_sort	Satisfacción del turista usando factores motivacionales: comparación de modelos de aprendizaje estadístico
dc.creator.fl_str_mv	Vanegas , Juan Gabriel Muñetón Santa , Guberney
dc.contributor.author.spa.fl_str_mv	Vanegas , Juan Gabriel Muñetón Santa , Guberney
dc.subject.eng.fl_str_mv	tourist satisfaction, tourist motivations, supervised learning, machine learning algorithms, Support Vector Machines, Medellín
topic	tourist satisfaction, tourist motivations, supervised learning, machine learning algorithms, Support Vector Machines, Medellín satisfacción del turista, motivaciones del turista, aprendizaje supervisado, algoritmos de aprendizaje estadístico, máquinas de soporte vectorial, Medellín
dc.subject.spa.fl_str_mv	satisfacción del turista, motivaciones del turista, aprendizaje supervisado, algoritmos de aprendizaje estadístico, máquinas de soporte vectorial, Medellín
description	El nivel de satisfacción de un turista con el destino visitado y su intención de volver a visitarlo se asumen como dependientes de su experiencia previa con el lugar. Para observar esta perspectiva relacional, se utilizó un conjunto de datos de 386 turistas que visitaron la ciudad de Medellín (Colombia) durante el año 2018. Para predecir la variable de volver a visitar la ciudad y la satisfacción con el destino, se usaron las variables consideradas de empuje (push) y aquellas que halan (pull) al turista. Se estimaron cuatro modelos de aprendizaje estadístico para la clasificación de los turistas: regresión logística, árboles aleatorios, máquinas de soporte vectorial y el conjunto de aumento de gradiente extremo. Las variables más importantes en las estimaciones de la satisfacción fueron ‘hablar sobre una experiencia de viaje en el futuro’ e ‘ir a lugares que mis amigos no han visitado’; y para volver a visitar la ciudad fueron ‘visitar lugares históricos’ y ‘viajar a bajos precios’.
publishDate	2023
dc.date.accessioned.none.fl_str_mv	2023-12-12T12:02:48Z 2024-06-07T10:33:13Z
dc.date.available.none.fl_str_mv	2023-12-12T12:02:48Z 2024-06-07T10:33:13Z
dc.date.issued.none.fl_str_mv	2023-12-12
dc.type.spa.fl_str_mv	Artículo de revista
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dc.type.local.eng.fl_str_mv	Journal article
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dc.identifier.doi.none.fl_str_mv	10.18601/01207555.n34.06
dc.identifier.eissn.none.fl_str_mv	2346-206X
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language	spa
dc.relation.bitstream.none.fl_str_mv	https://revistas.uexternado.edu.co/index.php/tursoc/article/download/9194/15281
dc.relation.citationedition.spa.fl_str_mv	, Año 2024 : Enero-Junio
dc.relation.citationendpage.none.fl_str_mv	178
dc.relation.citationstartpage.none.fl_str_mv	149
dc.relation.citationvolume.spa.fl_str_mv	34
dc.relation.ispartofjournal.spa.fl_str_mv	Turismo y Sociedad
dc.relation.references.spa.fl_str_mv	Abe, S. (2005). Support vector machines for pattern classification. Vol. 2. Springer. https://doi.org/10.1007/1-84628-219-5 Ahani, A., Nilashi, M., Ibrahim, O., Sanzogni, L., & Weaven, S. (2019). Market segmentation and travel choice prediction in Spa hotels through TripAdvisor’s online reviews. International Journal of Hospitality Management, 80, 52-77. https://doi.org/10.1016/j.ijhm.2019.01.003 Albayrak, T. & Caber, M. (2018). Examining the relationship between tourist motivation and satisfaction by two competing methods. Tourism Management, 69, 201-13. https://doi.org/10.1016/j. tourman.2018.06.015 Bloom, J. (2004). Tourist market segmentation with linear and non-linear techniques. Tourism Management, 25(6), 723-733. https://doi.org/10.1016/j.tourman.2003.07.004 Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32. ht tps://doi. org/10.1023/A:1010933404324 Chen, T. & Guestrin, C. (2016). xgboost: A scalable tree boosting system. En Association for Computing Machinery (Ed), Proceedings of the 22nd acm sigkdd International Conference on Knowledge Discovery and Data Mining (pp. 785-794). Association for Computing Machinery. https://doi.org/10.1145/2939672.2939785 Chen, Y., Zhang, H., & Qiu, L. (2013). Review on tourist satisfaction of tourism destinations. Journal of System and Management Sciences, 3(1), 74-86. http://www.aasmr.org/jsms/Vol3/No1/ jsms_Vol3_No1_8.pdf Chi, C. & Qu, H. (2008). Examining the structural relationships of destination image, tourist satis¬faction and destination loyalty: An integrated approach. Tourism Management, 29(4), 624-636. https://doi.org/10.1016/j.tourman.2007.06.007 Correia, A., Kozak, M., & Ferradeira, J. (2013). From tourist motivations to tourist satisfaction. International Journal of Culture, Tourism and Hospitality Research, 7(4), 411-424. https://doi. org/10.1108/ijcthr-05-2012-0022 Dean, D. & Suhartanto, D. (2019). The formation of visitor behavioral intention to creative tourism: The role of push–pull motivation. Asia Pacific Journal of Tourism Research, 24(5), 393-403. https:// doi.org/10.1080/10941665.2019.1572631 Deaton, A. (2013). The great escape. Princeton University Press. Do Valle, P., Silva, J., Mendes, J., & Guerreiro, M. (2006). Tourist satisfaction and destination loyalty intention: A structural and categorical analysis. International Journal of Business Science & Applied Management, 1(1), 25-44. https://acortar.link/C2k3Jh Egger, R. (2022). Machine learning in tourism: A brief overview. En R. Egger (Ed.), Applied data science in tourism: Interdisciplinary approaches, methodologies & applications (pp. 85-107). Springer. https://doi.org/10.1007/978-3-030-88389-8 Fodness, D. (1994). Measuring tourist motivation. Annals of Tourism Research, 21(3), 555-581. https://doi.org/10.1016/0160-7383(94)90120-1 Ghaderi, Z., Hatamifar, P., & Khalilzadeh, J. (2018). Analysis of tourist satisfaction in tourism supply chain management. Anatolia: An International Journal of Tourism and Hospitality Research, 29(3), 433-444. https://doi.org/10.1080/13032917.2018.1439074 Gil-León, J., Gutiérrez-Ayala, J., & Ramírez-Hernández, E. (2021). El papel del turismo patrimo¬nial en el índice de competitividad turística regional de Colombia: una evaluación de las relacio¬nes mediante PLS-PM. Revista Escuela de Administración de Negocios, (90), 169-192. https://doi.org/10.21158/01208160.n90.2021.2973 Guerra-Montenegro, J., Sánchez-Medina, J., Laña, I., Sánchez-Rodríguez, D. Alonso-González, I., & Del Ser, J. (2021). Computational Intelligence in the hospitality industry: A systematic literature review and a prospect of challenges. Applied Soft Computing, 102, 107082. https://doi.org/10.1016/j.asoc.2021.107082 Huang, Z., Kong, Y., & Zhou, C. (2018). A study on relationship between sports tourism motivation and tourists’ re-visiting intention: Based on Logistic Model. Advances in Social Science, Education and Humanities Research: Proceedings of the 2nd International Conference on Economics and Management, Education, Humanities and Social Sciences (EMEHSS 2018), 151, 54-61. https://doi.org/10.2991/emehss-18.2018.13 James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning with applications in R. Springer. https://www.stat.berkeley.edu/users/rabbee/s154/ISLR_First_Printing.pdf Jang, S. & Cai, L. (2002). Travel motivations and destination choice: A study of British outbound mar¬ket. Journal of Travel & Tourism Marketing, 13(3), 111-133. https://doi.org/10.1080/10548400209511570 Kahneman, D. (2011). Thinking, fast and slow. Farrar, Straus and Giroux. Kahneman, D., Sibony, O., & Sunstein, C. (2021). Noise: A flaw in human judgment. Hachette Book Group. Kozak, M. (2001). Comparative assessment of tourist satisfaction with destinations across two nationalities. Tourism Management, 22(4), 391-401. https://doi.org/10.1016/S0261-5177(00)00064-9 Kuhn, M. & Wickham, H. (2020). Tidymodels: A collection of packages for modeling and machine learning using tidyverse principles. https://www.tidymodels.org Kwon, W., Lee, M., & Back, K-J. (2020). Exploring the underlying factors of customer value in restaurants: A machine learning approach. International Journal of Hospitality Management, 91, 102643. https://doi.org/10.1016/j.ijhm.2020.102643 Lam-González, Y., León, C., & De León, J. (2019). Coopetition in maritime tourism: Assessing the effect of previous islands’ choice and experience in tourist satisfaction. Sustainability, 11(22), 6334. https://doi.org/10.3390/su11226334 Lee, T. (2009). A structural model to examine how destination image, attitude & moti¬vation affect the future behavior of tourists. Leisure Sciences, 31(3), 215-236. https://doi.org/10.1080/01490400902837787 Lee, G., O’Leary, J., Lee, S., & Morrison, A. (2002). Comparison and contrast of push and pull motivational effects on trip behavior: An application of a Multinomial Logistic Regression Model. Tourism Analysis, 7(2), 89-104. https://doi.org/10.3727/108354202108749970 Li, W., Xu, S., & Meng, W. (2009). A support vector machines method for tourist satisfaction degree evaluation. En IEEE Computer Society (Ed.), 2009 6th International Conference on Service Systems and Service Management (pp. 883-887). IEEE. https://doi.org/10.1109/icsssm.2009.5175007 Luna-Cortés, G. (2020). Análisis de la percepción de los estadounidenses que visitan Colombia. Un modelo de ecuaciones estructurales. Estudios y Perspectivas en Turismo, 29(1), 51-71. https://acortar.link/watpwr Mansfeld, Y. (1992). From motivation to actual travel. Annals of Tourism Research, 19(3), 399-419. https://doi.org/10.1016/0160-7383(92)90127-B Oh, H., Kim, B. Y., & Shin, J. H. (2004). Hospitality and tourism marketing: Recent developments in research and future directions. International Journal of Hospitality Management, 23(5), 425-447. https://doi.org/10.1016/j.ijhm.2004.10.004 Oh, H. & Lee, S. (2021). Evaluation and interpretation of tourist satisfaction for local Korean festivals using explainable AI. Sustainability, 13(19), 10901. https://doi.org/10.3390/su131910901 Olague de la Cruz, J. (2015). La imagen del destino y la motivación de viaje como determinantes de la satisfacción y lealtad del turismo urbano de ocio en Monterrey, México (Tesis doctoral, Universi¬dad Autónoma de Nuevo León). Repositorio Académico Digital UANL. http://eprints.uanl.mx/9248/ Prebensen, N., Skallerud, K., & Chen, J. (2010). Tourist motivation with sun and sand destinations: Satisfaction and the wom-effect. Journal of Travel & Tourism Marketing, 27(8), 858-873. https:// doi.org/10.1080/10548408.2010.527253 Salganik, M., Lundberg, I., Kindel, A., Ahearn, C., Al-Ghoneim, K., Almaatouq, A., Altschul, D., Brand, J., Bohme, N., Compton, R., Datta, D., Davidson, T., Filippova, A., Gilroy, C., Goode, B., Jahani, E., Kashyap, R., Kirchner, A. ... & McLanahan, S. (2020). Measuring the predictability of life outcomes with a scientific mass collaboration. PNAS: Proceedings of the National Academy of Sciences, 117(15), 8398-8403. https://doi.org/10.1073/pnas.2118703118 San Martín, H., Herrero, A., & García, M. (2019). An integrative model of destination brand equity and tourist satisfaction. Current Issues in Tourism, 22(16), 1992-2013. https://doi.org/10.1080/136 83500.2018.1428286 Schonlau, M. & Zou, R. (2020). The random forest algorithm for statistical learning. The Stata Journal, 20(1), 3-29. https://doi.org/10.1177%2F1536867X20909688 Song, Y., Wang, R., Fernández, J., & Li, D. (2021). Investigating sense of place of the Las Vegas Strip using online reviews and machine learning approaches. Landscape and Urban Planning, 205, 103956. https://doi.org/10.1016/j.landurbplan.2020.103956 Vapnik, V. (2000). The nature of statistical learning theory. Springer Science & Business Media. Villamediana-Pedrosa, J, Vila-López, N., & Küster-Boluda, I. (2020). Predictors of tourist enga¬gement: Travel motives and tourism destination profiles. Journal of Destination Marketing & Management, 16, 100412. https://doi.org/10.1016/j.jdmm.2020.100412 Wickham, H., Averick, M., Bryan, J., Chang, W., D’Agostino, L., François R., Grolemund, G., Hayes, A., Henry, L., Hester, J., Kuhn, M., Lin, T., Pedersen, T., Miller, E., Bache, S., Müller, K., Ooms, J., Robinson, D., ... Yutani, H. (2019). Welcome to the tidyverse. Journal of Open-Source Software, 4(43), 1686. https://doi.org/10.21105/joss.01686 Yoo, C., Yoon, D., & Park, E. (2018). Tourist motivation: An integral approach to destination choices. Tourism Review, 73(2), 169-185. https://doi.org/10.1108/TR-04-2017-0085 Yoon, Y. & Uysal, M. (2005). An examination of the effects of motivation and satisfaction on des¬tination loyalty: A structural model. Tourism Management, 26(1), 45-56. https://doi.org/10.1016/j. tourman.2003.08.016 Yu, L. & Goulden, M. (2006). A comparative analysis of international tourists’ satisfaction in Mongolia. Tourism Management, 27(6), 1331-1342. http://dx.doi.org/10.1016/j.tourman.2005.06.003 Żbikowski, K. & Antosiuk, P. (2021). A machine learning, bias-free approach for predicting business success using Crunchbase data. Information Processing & Management, 58(4), 102555. https://doi.org/10.1016/j.ipm.2021.102555
dc.rights.spa.fl_str_mv	Juan Gabriel Vanegas , Guberney Muñetón Santa - 2023
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spelling	Vanegas , Juan GabrielMuñetón Santa , Guberney2023-12-12T12:02:48Z2024-06-07T10:33:13Z2023-12-12T12:02:48Z2024-06-07T10:33:13Z2023-12-12El nivel de satisfacción de un turista con el destino visitado y su intención de volver a visitarlo se asumen como dependientes de su experiencia previa con el lugar. Para observar esta perspectiva relacional, se utilizó un conjunto de datos de 386 turistas que visitaron la ciudad de Medellín (Colombia) durante el año 2018. Para predecir la variable de volver a visitar la ciudad y la satisfacción con el destino, se usaron las variables consideradas de empuje (push) y aquellas que halan (pull) al turista. Se estimaron cuatro modelos de aprendizaje estadístico para la clasificación de los turistas: regresión logística, árboles aleatorios, máquinas de soporte vectorial y el conjunto de aumento de gradiente extremo. Las variables más importantes en las estimaciones de la satisfacción fueron ‘hablar sobre una experiencia de viaje en el futuro’ e ‘ir a lugares que mis amigos no han visitado’; y para volver a visitar la ciudad fueron ‘visitar lugares históricos’ y ‘viajar a bajos precios’.The level of satisfaction of a tourist with the destination visited, as well as his or her intention to revisit the destination, is assumed to be dependent on his or her previous experience with the place. To observe this relational perspective, a dataset of 386 tourists who visited the city of Medellin (Colombia) in 2018 was used. To predict the variables of revisiting the city and satisfaction with the destination, we consider push and pull variables. Four statistical learning models were estimated to classify tourists: Logistic Regression (LR), Random Forests (RF), Support Vector Machines (SVM), and the Extreme Gradient Boosting algorithm. The most important variables in the satisfaction estimation were: ‘talk about future travel experiences’ and ‘go to places my friends have not visited’, while for revisiting the city the variables were: ‘visit historical places’ and ‘travel at low prices’.application/pdf10.18601/01207555.n34.062346-206X0120-7555https://bdigital.uexternado.edu.co/handle/001/15596https://doi.org/10.18601/01207555.n34.06spaFacultad de Administración de Empresas Turísticas y Hotelerashttps://revistas.uexternado.edu.co/index.php/tursoc/article/download/9194/15281, Año 2024 : Enero-Junio17814934Turismo y SociedadAbe, S. (2005). Support vector machines for pattern classification. Vol. 2. Springer. https://doi.org/10.1007/1-84628-219-5Ahani, A., Nilashi, M., Ibrahim, O., Sanzogni, L., & Weaven, S. (2019). Market segmentation and travel choice prediction in Spa hotels through TripAdvisor’s online reviews. International Journal of Hospitality Management, 80, 52-77. https://doi.org/10.1016/j.ijhm.2019.01.003Albayrak, T. & Caber, M. (2018). Examining the relationship between tourist motivation and satisfaction by two competing methods. Tourism Management, 69, 201-13. https://doi.org/10.1016/j. tourman.2018.06.015Bloom, J. (2004). Tourist market segmentation with linear and non-linear techniques. Tourism Management, 25(6), 723-733. https://doi.org/10.1016/j.tourman.2003.07.004 Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32. ht tps://doi. org/10.1023/A:1010933404324Chen, T. & Guestrin, C. (2016). xgboost: A scalable tree boosting system. En Association for Computing Machinery (Ed), Proceedings of the 22nd acm sigkdd International Conference on Knowledge Discovery and Data Mining (pp. 785-794). Association for Computing Machinery. https://doi.org/10.1145/2939672.2939785Chen, Y., Zhang, H., & Qiu, L. (2013). Review on tourist satisfaction of tourism destinations. Journal of System and Management Sciences, 3(1), 74-86. http://www.aasmr.org/jsms/Vol3/No1/ jsms_Vol3_No1_8.pdfChi, C. & Qu, H. (2008). Examining the structural relationships of destination image, tourist satis¬faction and destination loyalty: An integrated approach. Tourism Management, 29(4), 624-636. https://doi.org/10.1016/j.tourman.2007.06.007Correia, A., Kozak, M., & Ferradeira, J. (2013). From tourist motivations to tourist satisfaction. International Journal of Culture, Tourism and Hospitality Research, 7(4), 411-424. https://doi. org/10.1108/ijcthr-05-2012-0022Dean, D. & Suhartanto, D. (2019). The formation of visitor behavioral intention to creative tourism: The role of push–pull motivation. Asia Pacific Journal of Tourism Research, 24(5), 393-403. https:// doi.org/10.1080/10941665.2019.1572631Deaton, A. (2013). The great escape. Princeton University Press.Do Valle, P., Silva, J., Mendes, J., & Guerreiro, M. (2006). Tourist satisfaction and destination loyalty intention: A structural and categorical analysis. International Journal of Business Science & Applied Management, 1(1), 25-44. https://acortar.link/C2k3JhEgger, R. (2022). Machine learning in tourism: A brief overview. En R. Egger (Ed.), Applied data science in tourism: Interdisciplinary approaches, methodologies & applications (pp. 85-107). Springer. https://doi.org/10.1007/978-3-030-88389-8Fodness, D. (1994). Measuring tourist motivation. Annals of Tourism Research, 21(3), 555-581. https://doi.org/10.1016/0160-7383(94)90120-1Ghaderi, Z., Hatamifar, P., & Khalilzadeh, J. (2018). Analysis of tourist satisfaction in tourism supply chain management. Anatolia: An International Journal of Tourism and Hospitality Research, 29(3), 433-444. https://doi.org/10.1080/13032917.2018.1439074Gil-León, J., Gutiérrez-Ayala, J., & Ramírez-Hernández, E. (2021). El papel del turismo patrimo¬nial en el índice de competitividad turística regional de Colombia: una evaluación de las relacio¬nes mediante PLS-PM. Revista Escuela de Administración de Negocios, (90), 169-192. https://doi.org/10.21158/01208160.n90.2021.2973Guerra-Montenegro, J., Sánchez-Medina, J., Laña, I., Sánchez-Rodríguez, D. Alonso-González, I., & Del Ser, J. (2021). Computational Intelligence in the hospitality industry: A systematic literature review and a prospect of challenges. Applied Soft Computing, 102, 107082. https://doi.org/10.1016/j.asoc.2021.107082Huang, Z., Kong, Y., & Zhou, C. (2018). A study on relationship between sports tourism motivation and tourists’ re-visiting intention: Based on Logistic Model. Advances in Social Science, Education and Humanities Research: Proceedings of the 2nd International Conference on Economics and Management, Education, Humanities and Social Sciences (EMEHSS 2018), 151, 54-61. https://doi.org/10.2991/emehss-18.2018.13James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning with applications in R. Springer. https://www.stat.berkeley.edu/users/rabbee/s154/ISLR_First_Printing.pdfJang, S. & Cai, L. (2002). Travel motivations and destination choice: A study of British outbound mar¬ket. Journal of Travel & Tourism Marketing, 13(3), 111-133. https://doi.org/10.1080/10548400209511570Kahneman, D. (2011). Thinking, fast and slow. Farrar, Straus and Giroux.Kahneman, D., Sibony, O., & Sunstein, C. (2021). Noise: A flaw in human judgment. Hachette Book Group.Kozak, M. (2001). Comparative assessment of tourist satisfaction with destinations across two nationalities. Tourism Management, 22(4), 391-401. https://doi.org/10.1016/S0261-5177(00)00064-9Kuhn, M. & Wickham, H. (2020). Tidymodels: A collection of packages for modeling and machine learning using tidyverse principles. https://www.tidymodels.orgKwon, W., Lee, M., & Back, K-J. (2020). Exploring the underlying factors of customer value in restaurants: A machine learning approach. International Journal of Hospitality Management, 91, 102643. https://doi.org/10.1016/j.ijhm.2020.102643Lam-González, Y., León, C., & De León, J. (2019). Coopetition in maritime tourism: Assessing the effect of previous islands’ choice and experience in tourist satisfaction. Sustainability, 11(22), 6334. https://doi.org/10.3390/su11226334Lee, T. (2009). A structural model to examine how destination image, attitude & moti¬vation affect the future behavior of tourists. Leisure Sciences, 31(3), 215-236. https://doi.org/10.1080/01490400902837787Lee, G., O’Leary, J., Lee, S., & Morrison, A. (2002). 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Information Processing & Management, 58(4), 102555. https://doi.org/10.1016/j.ipm.2021.102555Juan Gabriel Vanegas , Guberney Muñetón Santa - 2023info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.http://creativecommons.org/licenses/by-nc-sa/4.0https://revistas.uexternado.edu.co/index.php/tursoc/article/view/9194tourist satisfaction,tourist motivations,supervised learning,machine learning algorithms,Support Vector Machines,Medellínsatisfacción del turista,motivaciones del turista,aprendizaje supervisado,algoritmos de aprendizaje estadístico,máquinas de soporte vectorial,MedellínSatisfacción del turista usando factores motivacionales: comparación de modelos de aprendizaje estadísticoTourist Satisfaction Using Motivational Factors: Comparison of Statistical Learning ModelsArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85Textinfo:eu-repo/semantics/articleJournal articleinfo:eu-repo/semantics/publishedVersionPublicationOREORE.xmltext/xml2676https://bdigital.uexternado.edu.co/bitstreams/89854977-4c51-43df-8065-f38064303b1f/downloadec242eaf3de9c9b0a180619c010440b7MD51001/15596oai:bdigital.uexternado.edu.co:001/155962024-06-07 05:33:13.79http://creativecommons.org/licenses/by-nc-sa/4.0Juan Gabriel Vanegas , Guberney Muñetón Santa - 2023https://bdigital.uexternado.edu.coUniversidad Externado de Colombiametabiblioteca@metabiblioteca.org

Satisfacción del turista usando factores motivacionales: comparación de modelos de aprendizaje estadístico

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