Auswahl der wissenschaftlichen Literatur zum Thema „Random Forest predictive model“
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Zeitschriftenartikel zum Thema "Random Forest predictive model"
Mohnen, Sigrid M., Adriënne H. Rotteveel, Gerda Doornbos und Johan J. Polder. „Healthcare Expenditure Prediction with Neighbourhood Variables – A Random Forest Model“. Statistics, Politics and Policy 11, Nr. 2 (16.12.2020): 111–38. http://dx.doi.org/10.1515/spp-2019-0010.
Der volle Inhalt der QuelleWang, Fangyi, Yongchao Wang, Xiaokang Ji und Zhiping Wang. „Effective Macrosomia Prediction Using Random Forest Algorithm“. International Journal of Environmental Research and Public Health 19, Nr. 6 (10.03.2022): 3245. http://dx.doi.org/10.3390/ijerph19063245.
Der volle Inhalt der QuelleKor, 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.
Der volle Inhalt der QuelleRigatti, Steven J. „Random Forest“. Journal of Insurance Medicine 47, Nr. 1 (01.01.2017): 31–39. http://dx.doi.org/10.17849/insm-47-01-31-39.1.
Der volle Inhalt der QuelleWei, Li-Li, Yue-Shuai Pan, Yan Zhang, Kai Chen, Hao-Yu Wang und Jing-Yuan Wang. „Application of machine learning algorithm for predicting gestational diabetes mellitus in early pregnancy†“. Frontiers of Nursing 8, Nr. 3 (01.09.2021): 209–21. http://dx.doi.org/10.2478/fon-2021-0022.
Der volle Inhalt der QuelleDiaz, Pablo, Juan C. Salas, Aldo Cipriano und Felipe Núñez. „Random forest model predictive control for paste thickening“. Minerals Engineering 163 (März 2021): 106760. http://dx.doi.org/10.1016/j.mineng.2020.106760.
Der volle Inhalt der QuelleMao, Yiwen, und Asgeir Sorteberg. „Improving Radar-Based Precipitation Nowcasts with Machine Learning Using an Approach Based on Random Forest“. Weather and Forecasting 35, Nr. 6 (Dezember 2020): 2461–78. http://dx.doi.org/10.1175/waf-d-20-0080.1.
Der volle Inhalt der QuelleBashir Suleiman, Aminu, Stephen Luka und Muhammad Ibrahim. „CARDIOVASCULAR DISEASE PREDICTION USING RANDOM FOREST MACHINE LEARNING ALGORITHM“. FUDMA JOURNAL OF SCIENCES 7, Nr. 6 (31.12.2023): 282–89. http://dx.doi.org/10.33003/fjs-2023-0706-2128.
Der volle Inhalt der QuelleJeong, Hoyeon, Youngjune Kim und So Yeong Lim. „A Predictive Model for Farmland Purchase/Rent Using Random Forests“. Korean Agricultural Economics Association 63, Nr. 3 (30.09.2022): 153–68. http://dx.doi.org/10.24997/kjae.2022.63.3.153.
Der volle Inhalt der QuelleEmir, Senol, Hasan Dincer, Umit Hacioglu und Serhat Yuksel. „Random Regression Forest Model using Technical Analysis Variables“. International Journal of Finance & Banking Studies (2147-4486) 5, Nr. 3 (21.07.2016): 85–102. http://dx.doi.org/10.20525/ijfbs.v5i3.461.
Der volle Inhalt der QuelleDissertationen zum Thema "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.
Der volle Inhalt der QuelleStum, Alexander Knell. „Random Forests Applied as a Soil Spatial Predictive Model in Arid Utah“. DigitalCommons@USU, 2010. https://digitalcommons.usu.edu/etd/736.
Der volle Inhalt der QuelleKalmár, Marcus, und 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.
Der volle Inhalt der QuelleWagner, 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.
Der volle Inhalt der QuelleZhang, Yi. „Strategies for Combining Tree-Based Ensemble Models“. NSUWorks, 2017. http://nsuworks.nova.edu/gscis_etd/1021.
Der volle Inhalt der QuelleJonsson, Estrid, und 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.
Der volle Inhalt der QuelleAs 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.
Der volle Inhalt der QuelleVictors, Mason Lemoyne. „A Classification Tool for Predictive Data Analysis in Healthcare“. BYU ScholarsArchive, 2013. https://scholarsarchive.byu.edu/etd/5639.
Der volle Inhalt der QuelleOspina, Arango Juan David. „Predictive models for side effects following radiotherapy for prostate cancer“. Thesis, Rennes 1, 2014. http://www.theses.fr/2014REN1S046/document.
Der volle Inhalt der QuelleExternal 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.
Der volle Inhalt der QuelleBücher zum Thema "Random Forest predictive model"
Ravi, Margasahayam, und John F. Kennedy Space Center., Hrsg. Validation of a deterministic vibroacoustic response prediction model. Kennedy Space Center, Fla: National Aeronautics and Space Administration, John F. Kennedy Space Center, 1997.
Den vollen Inhalt der Quelle findenRavi, Margasahayam, und John F. Kennedy Space Center., Hrsg. Validation of a deterministic vibroacoustic response prediction model. Kennedy Space Center, Fla: National Aeronautics and Space Administration, John F. Kennedy Space Center, 1997.
Den vollen Inhalt der Quelle findenLópez, 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.
Den vollen Inhalt der Quelle findenTechnische Zuverlässigkeit 2021. VDI Verlag, 2021. http://dx.doi.org/10.51202/9783181023778.
Der volle Inhalt der QuelleAnderson, Raymond A. Credit Intelligence & Modelling. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780192844194.001.0001.
Der volle Inhalt der QuelleFrey, Ulrich. Sustainable Governance of Natural Resources. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780197502211.001.0001.
Der volle Inhalt der QuelleBuchteile zum Thema "Random Forest predictive model"
Al-Quraishi, Tahsien, Jemal H. Abawajy, Morshed U. Chowdhury, Sutharshan Rajasegarar und Ahmad Shaker Abdalrada. „Breast Cancer Recurrence Prediction Using Random Forest Model“. In 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.
Der volle Inhalt der QuelleVaiciukynaite, Egle, Ineta Zickute und Justas Salkevicius. „Solutions of Brand Posts on Facebook to Increase Customer Engagement Using the Random Forest Prediction Model“. In 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.
Der volle Inhalt der QuelleKhandelwal, Veena, und Shantanu Khandelwal. „Ground Water Quality Index Prediction Using Random Forest Model“. In 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.
Der volle Inhalt der QuelleDhamodaran, S., Ch Krishna Chaitanya Varma und Chittepu Dwarakanath Reddy. „Weather Prediction Model Using Random Forest Algorithm and GIS Data Model“. In 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.
Der volle Inhalt der QuelleWu, Jimmy Ming-Tai, Meng-Hsiun Tsai, Sheng-Han Xiao und Tsu-Yang Wu. „Construct Left Ventricular Hypertrophy Prediction Model Based on Random Forest“. In 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.
Der volle Inhalt der QuelleSong, Wanchao, und Yinghua Zhou. „Road Travel Time Prediction Method Based on Random Forest Model“. In Smart Innovation, Systems and Technologies, 155–63. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0077-0_17.
Der volle Inhalt der QuellePrasath, N., J. Sreemathy, N. Krishnaraj und P. Vigneshwaran. „Analysis of Crop Yield Prediction Using Random Forest Regression Model“. In Smart Innovation, Systems and Technologies, 239–49. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-7447-2_22.
Der volle Inhalt der QuelleLiu, Siqi, Hao Du und Mengling Feng. „Robust Predictive Models in Clinical Data—Random Forest and Support Vector Machines“. In 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.
Der volle Inhalt der QuelleXue, Ruixiang, und Hua Ding. „Risk Prediction of Corporate Earnings Manipulation Based on Random Forest Model“. In 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.
Der volle Inhalt der QuelleZhao, Zhijie, Wanting Zhou, Zeguo Qiu, Ang Li und Jiaying Wang. „Research on Ctrip Customer Churn Prediction Model Based on Random Forest“. In Business Intelligence and Information Technology, 511–23. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92632-8_48.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Random Forest predictive model"
Adeeyo, Yisa. „Random Forest Ensemble Model for Reservoir Fluid Property Prediction“. In SPE Nigeria Annual International Conference and Exhibition. SPE, 2022. http://dx.doi.org/10.2118/212044-ms.
Der volle Inhalt der QuelleZhu, Lin, Jiaxing Lu und Yihong Chen. „HDI-Forest: Highest Density Interval Regression Forest“. In 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.
Der volle Inhalt der QuelleRamadevi, R., V. J. K. Kishoresonti, M. Jain Jacob, V. Vaidehi, N. Mohankumar und M. Rajmohan. „Random Forest Predictive Model for Ventilator-Associated Pneumonia Prediction with IoT Data Analytics“. In 2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS). IEEE, 2024. http://dx.doi.org/10.1109/adics58448.2024.10533652.
Der volle Inhalt der QuelleS. Pahl, Eric, W. Nick Street, Hans J. Johnson und Alan I. Reed. „A Predictive Model for Kidney Transplant Graft Survival using Machine Learning“. In 4th International Conference on Computer Science and Information Technology (COMIT 2020). AIRCC Publishing Corporation, 2020. http://dx.doi.org/10.5121/csit.2020.101609.
Der volle Inhalt der QuelleZhang, Zhidong, Xiubin Zhu und Ding Liu. „Model of Gradient Boosting Random Forest Prediction“. In 2022 IEEE International Conference on Networking, Sensing and Control (ICNSC). IEEE, 2022. http://dx.doi.org/10.1109/icnsc55942.2022.10004112.
Der volle Inhalt der QuelleJoshi, Shreya, Rachana Ramesh und Shagufta Tahsildar. „A Bankruptcy Prediction Model Using Random Forest“. In 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, 2018. http://dx.doi.org/10.1109/iccons.2018.8663128.
Der volle Inhalt der QuelleRaut, Archana, Dipti Theng und Sarika Khandelwal. „Random Forest Regressor Model for Rainfall Prediction“. In 2023 International Conference on New Frontiers in Communication, Automation, Management and Security (ICCAMS). IEEE, 2023. http://dx.doi.org/10.1109/iccams60113.2023.10526085.
Der volle Inhalt der QuelleWang, Danqin, und Xiaolong Zhang. „Mobile user stability prediction with Random Forest model“. In 2014 International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 2014. http://dx.doi.org/10.1109/dsaa.2014.7058108.
Der volle Inhalt der QuelleMallahi, Imad El, Asmae Dlia, Jamal Riffi, Mohamed Adnane Mahraz und Hamid Tairi. „Prediction of Traffic Accidents using Random Forest Model“. In 2022 International Conference on Intelligent Systems and Computer Vision (ISCV). IEEE, 2022. http://dx.doi.org/10.1109/iscv54655.2022.9806099.
Der volle Inhalt der QuelleWen, Zhang, Zhaorui Jiang und Yutong Nie. „Wordle Distribution Prediction Model Based on Random Forest“. In 2023 IEEE 3rd International Conference on Data Science and Computer Application (ICDSCA). IEEE, 2023. http://dx.doi.org/10.1109/icdsca59871.2023.10393098.
Der volle Inhalt der QuelleBerichte der Organisationen zum Thema "Random Forest predictive model"
Ogunbire, Abimbola, Panick Kalambay, Hardik Gajera und Srinivas Pulugurtha. Deep Learning, Machine Learning, or Statistical Models for Weather-related Crash Severity Prediction. Mineta Transportation Institute, Dezember 2023. http://dx.doi.org/10.31979/mti.2023.2320.
Der volle Inhalt der QuelleZyphur, Michael. Dynamic Structural Equation Modeling in Mplus. Instats Inc., 2023. http://dx.doi.org/10.61700/aypvl8azm5nlr469.
Der volle Inhalt der QuelleLiu, Hongrui, und Rahul Ramachandra Shetty. Analytical Models for Traffic Congestion and Accident Analysis. Mineta Transportation Institute, November 2021. http://dx.doi.org/10.31979/mti.2021.2102.
Der volle Inhalt der QuelleMeidani, Hadi, und Amir Kazemi. Data-Driven Computational Fluid Dynamics Model for Predicting Drag Forces on Truck Platoons. Illinois Center for Transportation, November 2021. http://dx.doi.org/10.36501/0197-9191/21-036.
Der volle Inhalt der QuellePompeu, Gustavo, und José Luiz Rossi. Real/Dollar Exchange Rate Prediction Combining Machine Learning and Fundamental Models. Inter-American Development Bank, September 2022. http://dx.doi.org/10.18235/0004491.
Der volle Inhalt der QuelleWhite, Michael J., und Michelle E. Swearingen. Sound Propagation Through a Forest: A Predictive Model. Fort Belvoir, VA: Defense Technical Information Center, November 2004. http://dx.doi.org/10.21236/ada428938.
Der volle Inhalt der QuelleLi, Yuan, Benjamin Metcalf, Sopio Chochua, Zhongya Li, Robert Gertz, Hollis Walker, Paulina Hawkins, Theresa Tran, Lesley McGee und 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.), November 2017. http://dx.doi.org/10.15620/cdc/147467.
Der volle Inhalt der QuellePuttanapong, Nattapong, Arturo M. Martinez Jr, Mildred Addawe, Joseph Bulan, Ron Lester Durante und Marymell Martillan. Predicting Poverty Using Geospatial Data in Thailand. Asian Development Bank, Dezember 2020. http://dx.doi.org/10.22617/wps200434-2.
Der volle Inhalt der QuelleRossi, Jose Luiz, Carlos Piccioni, Marina Rossi und Daniel Cuajeiro. Brazilian Exchange Rate Forecasting in High Frequency. Inter-American Development Bank, September 2022. http://dx.doi.org/10.18235/0004488.
Der volle Inhalt der QuelleVas, Dragos, Elizabeth Corriveau, Lindsay Gaimaro und 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.), Dezember 2023. http://dx.doi.org/10.21079/11681/48018.
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