Predicción de impagos en correduría de seguros
dc.contributor.advisor | Garnica Alcázar, Óscar | |
dc.contributor.advisor | Velasco, José Manuel | |
dc.contributor.author | Hidalgo García, Javier | |
dc.date.accessioned | 2023-06-22T21:23:21Z | |
dc.date.available | 2023-06-22T21:23:21Z | |
dc.date.defense | 2022 | |
dc.date.issued | 2022-09 | |
dc.description.department | Depto. de Estadística y Ciencia de los Datos | |
dc.description.faculty | Fac. de Estudios Estadísticos | |
dc.description.refereed | TRUE | |
dc.description.status | unpub | |
dc.eprint.id | https://eprints.ucm.es/id/eprint/75164 | |
dc.identifier.uri | https://hdl.handle.net/20.500.14352/73991 | |
dc.language.iso | spa | |
dc.rights.accessRights | open access | |
dc.subject.cdu | 368.03 | |
dc.subject.cdu | 004.6 | |
dc.subject.ucm | Estadística | |
dc.subject.unesco | 1209 Estadística | |
dc.title | Predicción de impagos en correduría de seguros | |
dc.type | master thesis | |
dcterms.references | Bilgin, A., Ellson, J., Gansner, E., Hu, Y., North, S. y contribuciones. Graph- viz. https://graphviz.org/ Breiman, L. Bagging predictors. vol. 421, páginas –20, 1994. Chakure, A. Random forest regression. https://medium.com/swlh/random-forest-and-its-implementation-71824ced454f, 2019. Copeland, B. Artificial intelligence. Encyclopædia Britannica, (2), páginas –24, 2020. Cournapeau, D. Scikit-learn. https://scikit-earn.org/stable, 2012. GeeksforGeeks. Understanding logistic regression. https://www.geeksforgeeks.org/understanding-logistic-regression/, 2019. Glander, S. Machine learning basics - gradient boosting xgboost. https://www.shirin-glander.de/2018/11/ml_basics_gbm/, 2018. Herman, J., Usher, W., Mutel, C., Trindade, B., Hadka, D., Woodruff, M., Rios, F., Hyams, D. y xantares. Salib - sensitivity analysis library in python. https://salib.readthedocs.io/en/latest/ De la Hoz Manotas, A., De la Hoz Correa, E., Mendoza, F., Morales, R. y Sanchez, B. Obesity level estimation software based on decision trees. Journal of Computer Science, vol. 15, páginas –10, 2019. Iooss, B. y Lemaître, P. A Review on Global Sensitivity Analysis Methods, páginas 101–122. Springer US, Boston, MA, 2015. ISBN 978-1-4899-7547-8. Khalaf, M., Hussain, A. J., Keight, R., Al-Jumeily, D., Fergus, P., Keenan, R. y Tso, P. Machine learning approaches to the application of disease modifying therapy for sickle cell using classification models. Neurocomputing, vol. 228, páginas 154 – 164, 2017. ISSN 0925-2312. Advanced Intelligent Computing: Theory and Applications. Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A., van Ginneken, B. y Sánchez, C. I. A survey on deep learning in medical image analysis. Medical Image Analysis, vol. 42, páginas 60 – 88, 2017. ISSN 1361-8415. Lundberg, S. M. y Lee, S.-I. Shapley additive explanations. https://shap.readthedocs.io/en/latest/, 2017a. Lundberg, S. M. y Lee, S.-I. A unified approach to interpreting model predictions. En Advances in Neural Information Processing Systems (editado por I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan y R. Garnett), vol. 30, páginas 4765–4774. Curran Associates, Inc., 2017b. Maloney, K. O., Schmid, M. y Weller, D. E. Applying additive modelling and gradient boosting to assess the effects of watershed and reach characteristics on riverine assemblages. Methods in Ecology and Evolution, vol. 3(1), páginas 116–128, 2012. Marsland, S. Machine Learning: An Algorithmic Perspective, Second Edition. Chapman and amp; Hall/CRC, 2nd edición, 2014. ISBN 1466583282. McKinney, W. Pandas. https://pandas.pydata.org/ Muhamad Adnan, M. H. B., Husain, W. y Abdul Rashid, N. A hybrid approach using naïve bayes and genetic algorithm for childhood obesity prediction. En 2012 International Conference on Computer Information Science (ICCIS), vol. 1, páginas 281–285. 2012. Oliphant, T. Numpy. https://numpy.org/, 1995. Organization, W. H. Obesity: preventing and managing the global epidemic. 2000. Paul, S. Ensemble learning — bagging, boosting, stacking and casca- ding classifiers in machine learning using sklearn and mlextend libraries. https://medium.com/@saugata.paul1010/ensemble-learning-bagging-boosting-stacking- and-cascading-classifiers-in-machine-learning-9c66cb271674, 2018. Ministerio de Sanidad, C. y. B. S. Encuesta nacional de salud. españa 2017. https://www.mscbs.gob.es/estadEstudios/estadisticas/encuestaNacional/encuestaNac2017/ ENSE2017_notatecnica.pdf, 2017. Singh, B. y Tawfik, H. Machine learning approach for the early prediction of the risk of overweight and obesity in young people. En Computational Science – ICCS 2020 (editado por V. V. Krzhizhanovskaya, G. Závodszky, M. H. Lees, J. J. Dongarra, P. M. A. Sloot, S. Brissos y J. Teixeira), páginas 523–535. Springer International Publishing, Cham, 2020. ISBN 978-3-030-50423-6. Wickham, J., Stehman, S. y Homer, C. Spatial patterns of the United States national land cover dataset (nlcd) land-cover change thematic accuracy (2001–2011). Internatio- nal Journal of Remote Sensing, vol. 39, páginas 1729–1743, 2018. Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society: Series B (Methodological), 20(2), 215–232. Breiman, L. Bagging predictors. Machine Learning 24, 123–140 (1996). Springer Ed. https://doi.org/10.1007/BF00058655 Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785–794). New York, NY, USA: ACM. https://doi.org/10.1145/2939672.2939785 Scott M. Lundberg and Su-In Lee. 2017. A unified approach to interpreting model predictions. In Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS'17). Curran Associates Inc., Red Hook, NY, USA, 4768–4777. | |
dspace.entity.type | Publication |
Download
Original bundle
1 - 1 of 1
Loading...
- Name:
- TRABAJO TFM - JAVIER HIDALGO GARCIA.pdf
- Size:
- 2.04 MB
- Format:
- Adobe Portable Document Format