Literatura académica sobre el tema "XGBOOST MODEL"
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Artículos de revistas sobre el tema "XGBOOST MODEL"
Yang, Hao, Jiaxi Li, Siru Liu, Xiaoling Yang y Jialin Liu. "Predicting Risk of Hypoglycemia in Patients With Type 2 Diabetes by Electronic Health Record–Based Machine Learning: Development and Validation". JMIR Medical Informatics 10, n.º 6 (16 de junio de 2022): e36958. http://dx.doi.org/10.2196/36958.
Texto completoOUKHOUYA, HASSAN, HAMZA KADIRI, KHALID EL HIMDI y RABY GUERBAZ. "Forecasting International Stock Market Trends: XGBoost, LSTM, LSTM-XGBoost, and Backtesting XGBoost Models". Statistics, Optimization & Information Computing 12, n.º 1 (3 de noviembre de 2023): 200–209. http://dx.doi.org/10.19139/soic-2310-5070-1822.
Texto completoGu, Kai, Jianqi Wang, Hong Qian y Xiaoyan Su. "Study on Intelligent Diagnosis of Rotor Fault Causes with the PSO-XGBoost Algorithm". Mathematical Problems in Engineering 2021 (26 de abril de 2021): 1–17. http://dx.doi.org/10.1155/2021/9963146.
Texto completoLiu, Jialin, Jinfa Wu, Siru Liu, Mengdie Li, Kunchang Hu y Ke Li. "Predicting mortality of patients with acute kidney injury in the ICU using XGBoost model". PLOS ONE 16, n.º 2 (4 de febrero de 2021): e0246306. http://dx.doi.org/10.1371/journal.pone.0246306.
Texto completoJi, Shouwen, Xiaojing Wang, Wenpeng Zhao y Dong Guo. "An Application of a Three-Stage XGBoost-Based Model to Sales Forecasting of a Cross-Border E-Commerce Enterprise". Mathematical Problems in Engineering 2019 (16 de septiembre de 2019): 1–15. http://dx.doi.org/10.1155/2019/8503252.
Texto completoZhu, Yiming. "Stock Price Prediction based on LSTM and XGBoost Combination Model". Transactions on Computer Science and Intelligent Systems Research 1 (12 de octubre de 2023): 94–109. http://dx.doi.org/10.62051/z6dere47.
Texto completoXiong, Shuai, Zhixiang Liu, Chendi Min, Ying Shi, Shuangxia Zhang y Weijun Liu. "Compressive Strength Prediction of Cemented Backfill Containing Phosphate Tailings Using Extreme Gradient Boosting Optimized by Whale Optimization Algorithm". Materials 16, n.º 1 (28 de diciembre de 2022): 308. http://dx.doi.org/10.3390/ma16010308.
Texto completoWang, Yu, Li Guo, Yanrui Zhang y Xinyue Ma. "Research on CSI 300 Stock Index Price Prediction Based On EMD-XGBoost". Frontiers in Computing and Intelligent Systems 3, n.º 1 (17 de marzo de 2023): 72–77. http://dx.doi.org/10.54097/fcis.v3i1.6027.
Texto completoHarriz, Muhammad Alfathan, Nurhaliza Vania Akbariani, Harlis Setiyowati y Handri Santoso. "Enhancing the Efficiency of Jakarta's Mass Rapid Transit System with XGBoost Algorithm for Passenger Prediction". Jambura Journal of Informatics 5, n.º 1 (27 de abril de 2023): 1–6. http://dx.doi.org/10.37905/jji.v5i1.18814.
Texto completoSiringoringo, Rimbun, Resianta Perangin-angin y Jamaluddin Jamaluddin. "MODEL HIBRID GENETIC-XGBOOST DAN PRINCIPAL COMPONENT ANALYSIS PADA SEGMENTASI DAN PERAMALAN PASAR". METHOMIKA Jurnal Manajemen Informatika dan Komputerisasi Akuntansi 5, n.º 2 (31 de octubre de 2021): 97–103. http://dx.doi.org/10.46880/jmika.vol5no2.pp97-103.
Texto completoTesis sobre el tema "XGBOOST MODEL"
Matos, Sara Madeira. "Interpretable models of loss given default". Master's thesis, Instituto Superior de Economia e Gestão, 2021. http://hdl.handle.net/10400.5/20981.
Texto completoA gestão do risco de crédito é uma área em que os reguladores esperam que os bancos adotem modelos de risco transparentes e auditáveis colocando de parte o uso de modelos de black-box apesar destes serem mais precisos. Neste estudo, mostramos que os bancos não precisam de sacrificar a precisão preditiva ao custo da transparência do modelo para estar em conformidade com os requisitos regulatórios. Ilustramos isso mostrando que as previsões de perdas de crédito fornecidas por um modelo black-box podem ser facilmente explicadas em termos dos seus inputs.
Credit risk management is an area where regulators expect banks to have transparent and auditable risk models, which would preclude the use of more accurate black-box models. Furthermore, the opaqueness of these models may hide unknown biases that may lead to unfair lending decisions. In this study, we show that banks do not have to sacrifice predictive accuracy at the cost of model transparency to be compliant with regulatory requirements. We illustrate this by showing that the predictions of credit losses given by a black-box model can be easily explained in terms of their inputs. Because black-box models fit better the data, banks should consider the determinants of credit losses suggested by these models in lending decisions and pricing of credit exposures.
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Wigren, Richard y Filip Cornell. "Marketing Mix Modelling: A comparative study of statistical models". Thesis, Linköpings universitet, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-160082.
Texto completoPettersson, Gustav y John Almqvist. "Lavinprognoser och maskininlärning : Att prediktera lavinprognoser med maskininlärning och väderdata". Thesis, Uppsala universitet, Institutionen för informatik och media, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-387205.
Texto completoThis research project examines the feasibility of using machine learning to predict avalanche dangerby usingXGBoostand openly available weather data. Avalanche forecasts and meterological modelledweather data have been gathered for the six areas in Sweden where Naturvårdsverket throughlavin-prognoser.seissues avalanche forecasts. The avanlanche forecasts are collected fromlavinprognoser.seand the modelled weather data is collected from theMESANmodel, which is produced and providedby the Swedish Meteorological and Hydrological Institute. 40 machine learning models, in the form ofXGBoost, have been trained on this data set, with the goal of assessing the main aspects of an avalan-che forecast and the overall avalanche danger. The results show it is possible to predict the day to dayavalanche danger for the 2018/19 season inSödra Jämtlandsfjällenwith an accuracy of 71% and a MeanAverage Error of 0.256, by applying machine learning to the weather data for that region. The contribu-tion ofXGBoostin this context, is demonstrated by applying the simpler method ofLogistic Regressionon the data set and comparing the results. Thelogistic regressionperforms worse with an accuracy of56% and a Mean Average Error of 0.459. The contribution of this research is a proof of concept, showingfeasibility in predicting avalanche danger in Sweden, with the help of machine learning and weather data.
Karlsson, Henrik. "Uplift Modeling : Identifying Optimal Treatment Group Allocation and Whom to Contact to Maximize Return on Investment". Thesis, Linköpings universitet, Statistik och maskininlärning, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-157962.
Texto completoHenriksson, Erik y Kristopher Werlinder. "Housing Price Prediction over Countrywide Data : A comparison of XGBoost and Random Forest regressor models". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-302535.
Texto completoMålet med den här studien är att jämföra och undersöka hur en XGBoost regressor och en Random Forest regressor presterar i att förutsäga huspriser. Detta görs med hjälp av två stycken datauppsättningar. Jämförelsen tar hänsyn till modellernas träningstid, slutledningstid och de tre utvärderingsfaktorerna R2, RMSE and MAPE. Datauppsättningarna beskrivs i detalj tillsammans med en bakgrund om regressionsmodellerna. Metoden innefattar en rengöring av datauppsättningarna, sökande efter optimala hyperparametrar för modellerna och 5delad korsvalidering för att uppnå goda förutsägelser. Resultatet av studien är att XGBoost regressorn presterar bättre på både små och stora datauppsättningar, men att den är överlägsen när det gäller stora datauppsättningar. Medan Random Forest modellen kan uppnå liknande resultat som XGBoost modellen, tar träningstiden mellan 250 gånger så lång tid och modellen får en cirka 40 gånger längre slutledningstid. Detta gör att XGBoost är särskilt överlägsen vid användning av stora datauppsättningar.
Kinnander, Mathias. "Predicting profitability of new customers using gradient boosting tree models : Evaluating the predictive capabilities of the XGBoost, LightGBM and CatBoost algorithms". Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-19171.
Texto completoSvensson, William. "CAN STATISTICAL MODELS BEAT BENCHMARK PREDICTIONS BASED ON RANKINGS IN TENNIS?" Thesis, Uppsala universitet, Statistiska institutionen, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-447384.
Texto completoLiu, Xiaoyang. "Machine Learning Models in Fullerene/Metallofullerene Chromatography Studies". Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/93737.
Texto completoMachine learning models are capable to be applied in a wide range of areas, such as scientific research. In this thesis, machine learning models are applied to predict chromatography behaviors of fullerenes based on the molecular structures. Chromatography is a common technique for mixture separations, and the separation is because of the difference of interactions between molecules and a stationary phase. In real experiments, a mixture usually contains a large family of different compounds and it requires lots of work and resources to figure out the target compound. Therefore, models are extremely import for studies of chromatography. Traditional models are built based on physics rules, and involves several parameters. The physics parameters are measured by experiments or theoretically computed. However, both of them are time consuming and not easy to be conducted. For fullerenes, in my previous studies, it has been shown that the chromatography model can be simplified and only one parameter, polarizability, is required. A machine learning approach is introduced to enhance the model by predicting the molecular polarizabilities of fullerenes based on structures. The structure of a fullerene is represented by several local structures. Several types of machine learning models are built and tested on our data set and the result shows neural network gives the best predictions.
Sharma, Vibhor. "Early Stratification of Gestational Diabetes Mellitus (GDM) by building and evaluating machine learning models". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281398.
Texto completoGraviditetsdiabetes Mellitus (GDM), ett tillstånd som involverar onormala ni- våer av glukos i blodplasma har haft en snabb kraftig ökning bland de drab- bade mammorna som tillhör olika regioner och etniciteter runt om i världen. Den nuvarande metoden för screening och diagnos av GDM är begränsad till Oralt glukosetoleranstest (OGTT). Med tillkomsten av maskininlärningsalgo- ritmer har hälso- och sjukvården sett en ökning av maskininlärningsmetoder för sjukdomsdiagnos som alltmer används i en klinisk installation. Ändå inom GDM-området har det inte använts stor spridning av dessa algoritmer för att generera multiparametriska diagnostiska modeller för att hjälpa klinikerna för ovannämnda tillståndsdiagnos.I litteraturen finns det en uppenbar brist på tillämpning av maskininlär- ningsalgoritmer för GDM-diagnosen. Det har begränsats till den föreslagna användningen av några mycket enkla algoritmer som logistisk regression. Där- för har vi försökt att ta itu med detta forskningsgap genom att använda ett brett spektrum av maskininlärningsalgoritmer, kända för att vara effektiva för binär klassificering, för GDM-klassificering tidigt bland gesterande mamma. Det- ta kan hjälpa klinikerna för tidig diagnos av GDM och kommer att erbjuda chanser att mildra de negativa utfallen relaterade till GDM bland de dödande mamma och deras avkommor.Vi inrättade en empirisk studie för att undersöka prestandan för olika ma- skininlärningsalgoritmer som används specifikt för uppgiften att klassificera GDM. Dessa algoritmer tränades på en uppsättning valda prediktorvariabler av experterna. Jämfört sedan resultaten med de befintliga maskininlärnings- metoderna i litteraturen för GDM-klassificering baserat på en uppsättning pre- standametriker. Vår modell kunde inte överträffa de redan föreslagna maskininlärningsmodellerna för GDM-klassificering. Vi kunde tillskriva den valda uppsättningen prediktorvariabler och underrapportering av olika prestanda- metriker som precision i befintlig litteratur vilket leder till brist på informerad jämförelse.
Gregório, Rafael Leite. "Modelo híbrido de avaliação de risco de crédito para corporações brasileiras com base em algoritmos de aprendizado de máquina". Universidade Católica de Brasília, 2018. https://bdtd.ucb.br:8443/jspui/handle/tede/2432.
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The credit risk assessment has a relevant role for financial institutions because it is associated with possible losses and has a large impact on the balance sheets. Although there are several researches on applications of machine learning and finance models, a study is still lacking that integrates available knowledge about credit risk assessment. This paper aims at specifying the machine learning model of the probability of default of publicly traded companies present in the Bovespa Index (corporations) and, based on the estimations of the model, to obtain risk assessment metrics based on risk letters. We converged methodologies verified in the literature and we estimated models that comprise fundamentalist (balance sheet) and governance data, macroeconomic and even variables resulting from the application of the proprietary model of KMV credit risk assessment. We test the XGboost and LinearSVM algorithms, which have very different characteristics among them, but are potentially useful to the problem. Parameter Grids were performed to identify the most representative variables and to specify the best performing model. The model selected was XGboost, and performance was very similar to the results obtained for the North American stock market in analogous research. The estimated credit ratings suggest that they are more sensitive to the economic and financial situation of the companies than that verified by traditional Rating Agencies.
A avaliação do risco de crédito tem papel relevante para as instituições financeiras por estar associada a possíveis perdas que podem gerar grande impacto nos balanços. Embora existam várias pesquisas sobre aplicações de modelos de aprendizado de máquina e finanças, ainda não há estudo que integre o conhecimento disponível sobre avaliação de risco de crédito. Este trabalho visa especificar modelo de aprendizado de máquina da probabilidade de descumprimento de empresas de capital aberto presentes no Índice Bovespa (corporações) e, fruto das estimações do modelo, obter métrica de avaliação de risco baseada em letras (ratings) de risco. Convergiu-se metodologias verificadas na literatura e estimou-se modelos que compreendem componentes fundamentalistas (de balanço) e de governança corporativa, macroeconômicos e ainda variáveis produto da aplicação do modelo proprietário de avaliação de risco de crédito KMV. Testou-se os algoritmos XGboost e LinearSVM, os quais possuem características bastante distintas entre si, mas são potencialmente úteis ao problema exposto. Foram realizados Grids de parâmetros para identificação das variáveis mais representativas e para a especificação do modelo com melhor desempenho. O modelo selecionado foi o XGboost, tendo sido observado desempenho bastante semelhante aos resultados obtidos para o mercado de ações norte-americano em pesquisa análoga. Os ratings de crédito estimados mostram-se mais sensíveis à situação econômico-financeira das empresas ante o verificado por agências de rating tradicionais.
Libros sobre el tema "XGBOOST MODEL"
Nokeri, Tshepo Chris. Data Science Solutions with Python: Fast and Scalable Models Using Keras, Pyspark MLlib, H2O, XGBoost, and Scikit-Learn. Apress L. P., 2022.
Buscar texto completoCapítulos de libros sobre el tema "XGBOOST MODEL"
Saadat, Sumaya y V. Joseph Raymond. "Malware Classification Using CNN-XGBoost Model". En Artificial Intelligence Techniques for Advanced Computing Applications, 191–202. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5329-5_19.
Texto completoVenkat, Karthik, Tarika Gautam, Mohit Yadav y Mukhtiar Singh. "An XGBoost Ensemble Model for Residential Load Forecasting". En Advances in Intelligent Systems and Computing, 321–34. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8443-5_26.
Texto completoZhong, Weijian, Xiaoqin Lian, Chao Gao, Xiang Chen y Hongzhou Tan. "PM2.5 Concentration Prediction Based on mRMR-XGBoost Model". En Machine Learning and Intelligent Communications, 327–36. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04409-0_30.
Texto completoRen, Xudie, Haonan Guo, Shenghong Li, Shilin Wang y Jianhua Li. "A Novel Image Classification Method with CNN-XGBoost Model". En Digital Forensics and Watermarking, 378–90. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64185-0_28.
Texto completoChang, Wen-Chih, Yi-Hong Guo, Ya-Ling Yang, Ming-Chien Hsu, Yi-Hsuan Chu, Ting-Yi Chu y Long-Cheng Meng. "Using the XGBoost Model to Predict Santander Customer Trading". En Lecture Notes in Electrical Engineering, 115–24. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0115-6_11.
Texto completoYe, Lu. "Credit Rating of Chinese Companies Based on XGBoost Model". En New Perspectives and Paradigms in Applied Economics and Business, 99–111. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-23844-4_8.
Texto completoZolotareva, Ekaterina. "Aiding Long-Term Investment Decisions with XGBoost Machine Learning Model". En Artificial Intelligence and Soft Computing, 414–27. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87897-9_37.
Texto completoZolotareva, Ekaterina. "Aiding Long-Term Investment Decisions with XGBoost Machine Learning Model". En Artificial Intelligence and Soft Computing, 414–27. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87897-9_37.
Texto completoPetrovic, Aleksandar, Milos Antonijevic, Ivana Strumberger, Nebojsa Budimirovic, Nikola Savanovic y Stefana Janicijevic. "Intrusion Detection by XGBoost Model Tuned by Improved Multi-verse Optimizer". En Proceedings of the 1st International Conference on Innovation in Information Technology and Business (ICIITB 2022), 203–18. Dordrecht: Atlantis Press International BV, 2023. http://dx.doi.org/10.2991/978-94-6463-110-4_15.
Texto completoYu, Sun, Liwei Tian, Yijun Liu y Yuankai Guo. "LSTM-XGBoost Application of the Model to the Prediction of Stock Price". En Lecture Notes in Computer Science, 86–98. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78609-0_8.
Texto completoActas de conferencias sobre el tema "XGBOOST MODEL"
Zhaoweijie, Chenliang y Hujiangmin. "Forecast Rossmann Store Sales Base on Xgboost Model". En 2020 2nd International Conference on Economic Management and Model Engineering (ICEMME). IEEE, 2020. http://dx.doi.org/10.1109/icemme51517.2020.00110.
Texto completoZhang, Yixuan, Jialiang Tong, Ziyi Wang y Fengqiang Gao. "Customer Transaction Fraud Detection Using Xgboost Model". En 2020 International Conference on Computer Engineering and Application (ICCEA). IEEE, 2020. http://dx.doi.org/10.1109/iccea50009.2020.00122.
Texto completoWan, Fang. "XGBoost Based Supply Chain Fraud Detection Model". En 2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE). IEEE, 2021. http://dx.doi.org/10.1109/icbaie52039.2021.9390041.
Texto completoSiyuan, Liu, Liu Jingyuan, Gu Hangping y Ren Minhua. "Sleep staging prediction model based on XGBoost". En 2021 International Conference on Electronic Information Engineering and Computer Science (EIECS). IEEE, 2021. http://dx.doi.org/10.1109/eiecs53707.2021.9587974.
Texto completoLiu, Feng, Xiaowei Liu y Hao Yan. "Driving Style Identification Model based on XGBoost". En AIAM2021: 2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3495018.3495033.
Texto completoXiao, Bei, Peng-Cheng Luo, Zhi-Jun Cheng, Xiao-Nan Zhang y Xin-Wu Hu. "Systematic Combat Effectiveness Evaluation Model Based on Xgboost". En 2018 12th International Conference on Reliability, Maintainability, and Safety (ICRMS). IEEE, 2018. http://dx.doi.org/10.1109/icrms.2018.00033.
Texto completoBa Alawi, Abdulfattah E., Ferhat Bozkurt y Faruk Baturalp. "Xgboost-Based Multi-Steps Cybersecurity Attacks Detection Model". En 2023 3rd International Conference on Emerging Smart Technologies and Applications (eSmarTA). IEEE, 2023. http://dx.doi.org/10.1109/esmarta59349.2023.10293597.
Texto completoZhang, Yibin, Chunyan Shao y Chen Zou. "Prediction of Customers’ Behaviors Based on XGBoost Model". En 2023 2nd International Conference on Data Analytics, Computing and Artificial Intelligence (ICDACAI). IEEE, 2023. http://dx.doi.org/10.1109/icdacai59742.2023.00076.
Texto completoRibeiro, Matheus Henrique Dal Molin, Ramon Gomes Silva, Viviana Cocco Mariani y Leandro dos Santos Coelho. "Dengue Cases Forecasting Based on eXtreme Gradient Boosting Ensemble with Coyote Optimization". En Congresso Brasileiro de Inteligência Computacional. SBIC, 2021. http://dx.doi.org/10.21528/cbic2021-36.
Texto completoMa, Tao, Yusen Zhang, Xiangxin Nie, Xinchao Zhao y Yexing Li. "An XGBoost-based Electric Vehicle Battery Consumption Prediction Model". En 2021 IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS). IEEE, 2021. http://dx.doi.org/10.1109/icpics52425.2021.9524291.
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