Littérature scientifique sur le sujet « Random Forest predictive model »
Créez une référence correcte selon les styles APA, MLA, Chicago, Harvard et plusieurs autres
Sommaire
Consultez les listes thématiques d’articles de revues, de livres, de thèses, de rapports de conférences et d’autres sources académiques sur le sujet « Random Forest predictive model ».
À côté de chaque source dans la liste de références il y a un bouton « Ajouter à la bibliographie ». Cliquez sur ce bouton, et nous générerons automatiquement la référence bibliographique pour la source choisie selon votre style de citation préféré : APA, MLA, Harvard, Vancouver, Chicago, etc.
Vous pouvez aussi télécharger le texte intégral de la publication scolaire au format pdf et consulter son résumé en ligne lorsque ces informations sont inclues dans les métadonnées.
Articles de revues sur le sujet "Random Forest predictive model"
Mohnen, Sigrid M., Adriënne H. Rotteveel, Gerda Doornbos et Johan J. Polder. « Healthcare Expenditure Prediction with Neighbourhood Variables – A Random Forest Model ». Statistics, Politics and Policy 11, no 2 (16 décembre 2020) : 111–38. http://dx.doi.org/10.1515/spp-2019-0010.
Texte intégralWang, Fangyi, Yongchao Wang, Xiaokang Ji et Zhiping Wang. « Effective Macrosomia Prediction Using Random Forest Algorithm ». International Journal of Environmental Research and Public Health 19, no 6 (10 mars 2022) : 3245. http://dx.doi.org/10.3390/ijerph19063245.
Texte intégralKor, Hakan. « Global solar radiation prediction model with random forest algorithm ». Thermal Science 25, Spec. issue 1 (2021) : 31–39. http://dx.doi.org/10.2298/tsci200608004k.
Texte intégralRigatti, Steven J. « Random Forest ». Journal of Insurance Medicine 47, no 1 (1 janvier 2017) : 31–39. http://dx.doi.org/10.17849/insm-47-01-31-39.1.
Texte intégralWei, Li-Li, Yue-Shuai Pan, Yan Zhang, Kai Chen, Hao-Yu Wang et Jing-Yuan Wang. « Application of machine learning algorithm for predicting gestational diabetes mellitus in early pregnancy† ». Frontiers of Nursing 8, no 3 (1 septembre 2021) : 209–21. http://dx.doi.org/10.2478/fon-2021-0022.
Texte intégralDiaz, Pablo, Juan C. Salas, Aldo Cipriano et Felipe Núñez. « Random forest model predictive control for paste thickening ». Minerals Engineering 163 (mars 2021) : 106760. http://dx.doi.org/10.1016/j.mineng.2020.106760.
Texte intégralMao, Yiwen, et Asgeir Sorteberg. « Improving Radar-Based Precipitation Nowcasts with Machine Learning Using an Approach Based on Random Forest ». Weather and Forecasting 35, no 6 (décembre 2020) : 2461–78. http://dx.doi.org/10.1175/waf-d-20-0080.1.
Texte intégralBashir Suleiman, Aminu, Stephen Luka et Muhammad Ibrahim. « CARDIOVASCULAR DISEASE PREDICTION USING RANDOM FOREST MACHINE LEARNING ALGORITHM ». FUDMA JOURNAL OF SCIENCES 7, no 6 (31 décembre 2023) : 282–89. http://dx.doi.org/10.33003/fjs-2023-0706-2128.
Texte intégralJeong, Hoyeon, Youngjune Kim et So Yeong Lim. « A Predictive Model for Farmland Purchase/Rent Using Random Forests ». Korean Agricultural Economics Association 63, no 3 (30 septembre 2022) : 153–68. http://dx.doi.org/10.24997/kjae.2022.63.3.153.
Texte intégralEmir, Senol, Hasan Dincer, Umit Hacioglu et Serhat Yuksel. « Random Regression Forest Model using Technical Analysis Variables ». International Journal of Finance & ; Banking Studies (2147-4486) 5, no 3 (21 juillet 2016) : 85–102. http://dx.doi.org/10.20525/ijfbs.v5i3.461.
Texte intégralThèses sur le sujet "Random Forest predictive model"
Palczewska, Anna Maria. « Interpretation, Identification and Reuse of Models. Theory and algorithms with applications in predictive toxicology ». Thesis, University of Bradford, 2014. http://hdl.handle.net/10454/7349.
Texte intégralStum, Alexander Knell. « Random Forests Applied as a Soil Spatial Predictive Model in Arid Utah ». DigitalCommons@USU, 2010. https://digitalcommons.usu.edu/etd/736.
Texte intégralKalmár, Marcus, et Joel Nilsson. « The art of forecasting – an analysis of predictive precision of machine learning models ». Thesis, Uppsala universitet, Statistiska institutionen, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-280675.
Texte intégralWagner, Christopher. « Regression Model to Project and Mitigate Vehicular Emissions in Cochabamba, Bolivia ». University of Dayton / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1501719312999566.
Texte intégralZhang, Yi. « Strategies for Combining Tree-Based Ensemble Models ». NSUWorks, 2017. http://nsuworks.nova.edu/gscis_etd/1021.
Texte intégralJonsson, Estrid, et Sara Fredrikson. « An Investigation of How Well Random Forest Regression Can Predict Demand : Is Random Forest Regression better at predicting the sell-through of close to date products at different discount levels than a basic linear model ? » Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-302025.
Texte intégralAs the climate crisis continues to evolve many companies focus their development on becoming more sustainable. With greenhouse gases being highlighted as the main problem, food waste has obtained a great deal of attention after being named the third largest contributor to global emissions. One way retailers have attempted to improve is through offering close-to-date produce at discount, hence decreasing levels of food being thrown away. To minimize waste the level of discount must be optimized, and as the products can be seen as flawed the known price-to-demand relation of the products may be insufficient. The optimization process historically involves generalized linear regression models, however demand is a complex concept influenced by many factors. This report investigates whether a Machine Learning model, Random Forest Regression, is better at estimating the demand of close-to-date products at different discount levels than a basic linear regression model. The discussion also includes an analysis on whether discounts always increase the will to buy and whether this depends on product type. The results show that Random Forest to a greater extent considers the many factors influencing demand and is superior as a predictor in this case. Furthermore it was concluded that there is generally not a clear linear relation however this does depend on product type as certain categories showed some linearity.
Mathis, Tyler Alan. « Predicting Hardness of Friction Stir Processed 304L Stainless Steel using a Finite Element Model and a Random Forest Algorithm ». BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/7591.
Texte intégralVictors, Mason Lemoyne. « A Classification Tool for Predictive Data Analysis in Healthcare ». BYU ScholarsArchive, 2013. https://scholarsarchive.byu.edu/etd/5639.
Texte intégralOspina, Arango Juan David. « Predictive models for side effects following radiotherapy for prostate cancer ». Thesis, Rennes 1, 2014. http://www.theses.fr/2014REN1S046/document.
Texte intégralExternal beam radiotherapy (EBRT) is one of the cornerstones of prostate cancer treatment. The objectives of radiotherapy are, firstly, to deliver a high dose of radiation to the tumor (prostate and seminal vesicles) in order to achieve a maximal local control and, secondly, to spare the neighboring organs (mainly the rectum and the bladder) to avoid normal tissue complications. Normal tissue complication probability (NTCP) models are then needed to assess the feasibility of the treatment and inform the patient about the risk of side effects, to derive dose-Volume constraints and to compare different treatments. In the context of EBRT, the objectives of this thesis were to find predictors of bladder and rectal complications following treatment; to develop new NTCP models that allow for the integration of both dosimetric and patient parameters; to compare the predictive capabilities of these new models to the classic NTCP models and to develop new methodologies to identify dose patterns correlated to normal complications following EBRT for prostate cancer treatment. A large cohort of patient treated by conformal EBRT for prostate caner under several prospective French clinical trials was used for the study. In a first step, the incidence of the main genitourinary and gastrointestinal symptoms have been described. With another classical approach, namely logistic regression, some predictors of genitourinary and gastrointestinal complications were identified. The logistic regression models were then graphically represented to obtain nomograms, a graphical tool that enables clinicians to rapidly assess the complication risks associated with a treatment and to inform patients. This information can be used by patients and clinicians to select a treatment among several options (e.g. EBRT or radical prostatectomy). In a second step, we proposed the use of random forest, a machine-Learning technique, to predict the risk of complications following EBRT for prostate cancer. The superiority of the random forest NTCP, assessed by the area under the curve (AUC) of the receiving operative characteristic (ROC) curve, was established. In a third step, the 3D dose distribution was studied. A 2D population value decomposition (PVD) technique was extended to a tensorial framework to be applied on 3D volume image analysis. Using this tensorial PVD, a population analysis was carried out to find a pattern of dose possibly correlated to a normal tissue complication following EBRT. Also in the context of 3D image population analysis, a spatio-Temporal nonparametric mixed-Effects model was developed. This model was applied to find an anatomical region where the dose could be correlated to a normal tissue complication following EBRT
Kabir, Mitra. « Prediction of mammalian essential genes based on sequence and functional features ». Thesis, University of Manchester, 2017. https://www.research.manchester.ac.uk/portal/en/theses/prediction-of-mammalian-essential-genes-based-on-sequence-and-functional-features(cf8eeed5-c2b3-47c3-9a8f-2cc290c90d56).html.
Texte intégralLivres sur le sujet "Random Forest predictive model"
Ravi, Margasahayam, et John F. Kennedy Space Center., dir. Validation of a deterministic vibroacoustic response prediction model. Kennedy Space Center, Fla : National Aeronautics and Space Administration, John F. Kennedy Space Center, 1997.
Trouver le texte intégralRavi, Margasahayam, et John F. Kennedy Space Center., dir. Validation of a deterministic vibroacoustic response prediction model. Kennedy Space Center, Fla : National Aeronautics and Space Administration, John F. Kennedy Space Center, 1997.
Trouver le texte intégralLópez, César Pérez. DATA MINING and MACHINE LEARNING. PREDICTIVE TECHNIQUES : ENSEMBLE METHODS, BOOSTING, BAGGING, RANDOM FOREST, DECISION TREES and REGRESSION TREES. : Examples with MATLAB. Lulu Press, Inc., 2021.
Trouver le texte intégralTechnische Zuverlässigkeit 2021. VDI Verlag, 2021. http://dx.doi.org/10.51202/9783181023778.
Texte intégralAnderson, Raymond A. Credit Intelligence & ; Modelling. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780192844194.001.0001.
Texte intégralFrey, Ulrich. Sustainable Governance of Natural Resources. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780197502211.001.0001.
Texte intégralChapitres de livres sur le sujet "Random Forest predictive model"
Al-Quraishi, Tahsien, Jemal H. Abawajy, Morshed U. Chowdhury, Sutharshan Rajasegarar et Ahmad Shaker Abdalrada. « Breast Cancer Recurrence Prediction Using Random Forest Model ». Dans Advances in Intelligent Systems and Computing, 318–29. Cham : Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-72550-5_31.
Texte intégralVaiciukynaite, Egle, Ineta Zickute et Justas Salkevicius. « Solutions of Brand Posts on Facebook to Increase Customer Engagement Using the Random Forest Prediction Model ». Dans FGF Studies in Small Business and Entrepreneurship, 191–214. Cham : Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11371-0_9.
Texte intégralKhandelwal, Veena, et Shantanu Khandelwal. « Ground Water Quality Index Prediction Using Random Forest Model ». Dans Proceedings of International Conference on Recent Trends in Computing, 469–77. Singapore : Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8825-7_40.
Texte intégralDhamodaran, S., Ch Krishna Chaitanya Varma et Chittepu Dwarakanath Reddy. « Weather Prediction Model Using Random Forest Algorithm and GIS Data Model ». Dans Innovative Data Communication Technologies and Application, 306–11. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38040-3_35.
Texte intégralWu, Jimmy Ming-Tai, Meng-Hsiun Tsai, Sheng-Han Xiao et Tsu-Yang Wu. « Construct Left Ventricular Hypertrophy Prediction Model Based on Random Forest ». Dans Recent Advances in Intelligent Information Hiding and Multimedia Signal Processing, 142–50. Cham : Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03745-1_18.
Texte intégralSong, Wanchao, et Yinghua Zhou. « Road Travel Time Prediction Method Based on Random Forest Model ». Dans Smart Innovation, Systems and Technologies, 155–63. Singapore : Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0077-0_17.
Texte intégralPrasath, N., J. Sreemathy, N. Krishnaraj et P. Vigneshwaran. « Analysis of Crop Yield Prediction Using Random Forest Regression Model ». Dans Smart Innovation, Systems and Technologies, 239–49. Singapore : Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-7447-2_22.
Texte intégralLiu, Siqi, Hao Du et Mengling Feng. « Robust Predictive Models in Clinical Data—Random Forest and Support Vector Machines ». Dans Leveraging Data Science for Global Health, 219–28. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47994-7_13.
Texte intégralXue, Ruixiang, et Hua Ding. « Risk Prediction of Corporate Earnings Manipulation Based on Random Forest Model ». Dans Application of Intelligent Systems in Multi-modal Information Analytics, 100–107. Cham : Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05237-8_13.
Texte intégralZhao, Zhijie, Wanting Zhou, Zeguo Qiu, Ang Li et Jiaying Wang. « Research on Ctrip Customer Churn Prediction Model Based on Random Forest ». Dans Business Intelligence and Information Technology, 511–23. Cham : Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92632-8_48.
Texte intégralActes de conférences sur le sujet "Random Forest predictive model"
Adeeyo, Yisa. « Random Forest Ensemble Model for Reservoir Fluid Property Prediction ». Dans SPE Nigeria Annual International Conference and Exhibition. SPE, 2022. http://dx.doi.org/10.2118/212044-ms.
Texte intégralZhu, Lin, Jiaxing Lu et Yihong Chen. « HDI-Forest : Highest Density Interval Regression Forest ». Dans Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California : International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/621.
Texte intégralRamadevi, R., V. J. K. Kishoresonti, M. Jain Jacob, V. Vaidehi, N. Mohankumar et M. Rajmohan. « Random Forest Predictive Model for Ventilator-Associated Pneumonia Prediction with IoT Data Analytics ». Dans 2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS). IEEE, 2024. http://dx.doi.org/10.1109/adics58448.2024.10533652.
Texte intégralS. Pahl, Eric, W. Nick Street, Hans J. Johnson et Alan I. Reed. « A Predictive Model for Kidney Transplant Graft Survival using Machine Learning ». Dans 4th International Conference on Computer Science and Information Technology (COMIT 2020). AIRCC Publishing Corporation, 2020. http://dx.doi.org/10.5121/csit.2020.101609.
Texte intégralZhang, Zhidong, Xiubin Zhu et Ding Liu. « Model of Gradient Boosting Random Forest Prediction ». Dans 2022 IEEE International Conference on Networking, Sensing and Control (ICNSC). IEEE, 2022. http://dx.doi.org/10.1109/icnsc55942.2022.10004112.
Texte intégralJoshi, Shreya, Rachana Ramesh et Shagufta Tahsildar. « A Bankruptcy Prediction Model Using Random Forest ». Dans 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, 2018. http://dx.doi.org/10.1109/iccons.2018.8663128.
Texte intégralRaut, Archana, Dipti Theng et Sarika Khandelwal. « Random Forest Regressor Model for Rainfall Prediction ». Dans 2023 International Conference on New Frontiers in Communication, Automation, Management and Security (ICCAMS). IEEE, 2023. http://dx.doi.org/10.1109/iccams60113.2023.10526085.
Texte intégralWang, Danqin, et Xiaolong Zhang. « Mobile user stability prediction with Random Forest model ». Dans 2014 International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 2014. http://dx.doi.org/10.1109/dsaa.2014.7058108.
Texte intégralMallahi, Imad El, Asmae Dlia, Jamal Riffi, Mohamed Adnane Mahraz et Hamid Tairi. « Prediction of Traffic Accidents using Random Forest Model ». Dans 2022 International Conference on Intelligent Systems and Computer Vision (ISCV). IEEE, 2022. http://dx.doi.org/10.1109/iscv54655.2022.9806099.
Texte intégralWen, Zhang, Zhaorui Jiang et Yutong Nie. « Wordle Distribution Prediction Model Based on Random Forest ». Dans 2023 IEEE 3rd International Conference on Data Science and Computer Application (ICDSCA). IEEE, 2023. http://dx.doi.org/10.1109/icdsca59871.2023.10393098.
Texte intégralRapports d'organisations sur le sujet "Random Forest predictive model"
Ogunbire, Abimbola, Panick Kalambay, Hardik Gajera et Srinivas Pulugurtha. Deep Learning, Machine Learning, or Statistical Models for Weather-related Crash Severity Prediction. Mineta Transportation Institute, décembre 2023. http://dx.doi.org/10.31979/mti.2023.2320.
Texte intégralZyphur, Michael. Dynamic Structural Equation Modeling in Mplus. Instats Inc., 2023. http://dx.doi.org/10.61700/aypvl8azm5nlr469.
Texte intégralLiu, Hongrui, et Rahul Ramachandra Shetty. Analytical Models for Traffic Congestion and Accident Analysis. Mineta Transportation Institute, novembre 2021. http://dx.doi.org/10.31979/mti.2021.2102.
Texte intégralMeidani, Hadi, et Amir Kazemi. Data-Driven Computational Fluid Dynamics Model for Predicting Drag Forces on Truck Platoons. Illinois Center for Transportation, novembre 2021. http://dx.doi.org/10.36501/0197-9191/21-036.
Texte intégralPompeu, Gustavo, et José Luiz Rossi. Real/Dollar Exchange Rate Prediction Combining Machine Learning and Fundamental Models. Inter-American Development Bank, septembre 2022. http://dx.doi.org/10.18235/0004491.
Texte intégralWhite, Michael J., et Michelle E. Swearingen. Sound Propagation Through a Forest : A Predictive Model. Fort Belvoir, VA : Defense Technical Information Center, novembre 2004. http://dx.doi.org/10.21236/ada428938.
Texte intégralLi, Yuan, Benjamin Metcalf, Sopio Chochua, Zhongya Li, Robert Gertz, Hollis Walker, Paulina Hawkins, Theresa Tran, Lesley McGee et Bernard W. Beall. Validation of β-lactam minimum inhibitory concentration predictions for pneumococcal isolates with newly encountered penicillin binding protein (PBP) sequences [Supporting data]. Centers for Disease Control and Prevention (U.S.), novembre 2017. http://dx.doi.org/10.15620/cdc/147467.
Texte intégralPuttanapong, Nattapong, Arturo M. Martinez Jr, Mildred Addawe, Joseph Bulan, Ron Lester Durante et Marymell Martillan. Predicting Poverty Using Geospatial Data in Thailand. Asian Development Bank, décembre 2020. http://dx.doi.org/10.22617/wps200434-2.
Texte intégralRossi, Jose Luiz, Carlos Piccioni, Marina Rossi et Daniel Cuajeiro. Brazilian Exchange Rate Forecasting in High Frequency. Inter-American Development Bank, septembre 2022. http://dx.doi.org/10.18235/0004488.
Texte intégralVas, Dragos, Elizabeth Corriveau, Lindsay Gaimaro et Robyn Barbato. Challenges and limitations of using autonomous instrumentation for measuring in situ soil respiration in a subarctic boreal forest in Alaska, USA. Engineer Research and Development Center (U.S.), décembre 2023. http://dx.doi.org/10.21079/11681/48018.
Texte intégral