Academic literature on the topic 'Random Forest predictive model'
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Journal articles on the topic "Random Forest predictive model"
Mohnen, Sigrid M., Adriënne H. Rotteveel, Gerda Doornbos, and Johan J. Polder. "Healthcare Expenditure Prediction with Neighbourhood Variables – A Random Forest Model." Statistics, Politics and Policy 11, no. 2 (December 16, 2020): 111–38. http://dx.doi.org/10.1515/spp-2019-0010.
Full textWang, Fangyi, Yongchao Wang, Xiaokang Ji, and Zhiping Wang. "Effective Macrosomia Prediction Using Random Forest Algorithm." International Journal of Environmental Research and Public Health 19, no. 6 (March 10, 2022): 3245. http://dx.doi.org/10.3390/ijerph19063245.
Full textKor, 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.
Full textRigatti, Steven J. "Random Forest." Journal of Insurance Medicine 47, no. 1 (January 1, 2017): 31–39. http://dx.doi.org/10.17849/insm-47-01-31-39.1.
Full textWei, Li-Li, Yue-Shuai Pan, Yan Zhang, Kai Chen, Hao-Yu Wang, and Jing-Yuan Wang. "Application of machine learning algorithm for predicting gestational diabetes mellitus in early pregnancy†." Frontiers of Nursing 8, no. 3 (September 1, 2021): 209–21. http://dx.doi.org/10.2478/fon-2021-0022.
Full textDiaz, Pablo, Juan C. Salas, Aldo Cipriano, and Felipe Núñez. "Random forest model predictive control for paste thickening." Minerals Engineering 163 (March 2021): 106760. http://dx.doi.org/10.1016/j.mineng.2020.106760.
Full textMao, Yiwen, and Asgeir Sorteberg. "Improving Radar-Based Precipitation Nowcasts with Machine Learning Using an Approach Based on Random Forest." Weather and Forecasting 35, no. 6 (December 2020): 2461–78. http://dx.doi.org/10.1175/waf-d-20-0080.1.
Full textBashir Suleiman, Aminu, Stephen Luka, and Muhammad Ibrahim. "CARDIOVASCULAR DISEASE PREDICTION USING RANDOM FOREST MACHINE LEARNING ALGORITHM." FUDMA JOURNAL OF SCIENCES 7, no. 6 (December 31, 2023): 282–89. http://dx.doi.org/10.33003/fjs-2023-0706-2128.
Full textJeong, Hoyeon, Youngjune Kim, and So Yeong Lim. "A Predictive Model for Farmland Purchase/Rent Using Random Forests." Korean Agricultural Economics Association 63, no. 3 (September 30, 2022): 153–68. http://dx.doi.org/10.24997/kjae.2022.63.3.153.
Full textEmir, Senol, Hasan Dincer, Umit Hacioglu, and Serhat Yuksel. "Random Regression Forest Model using Technical Analysis Variables." International Journal of Finance & Banking Studies (2147-4486) 5, no. 3 (July 21, 2016): 85–102. http://dx.doi.org/10.20525/ijfbs.v5i3.461.
Full textDissertations / Theses on the topic "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.
Full textStum, Alexander Knell. "Random Forests Applied as a Soil Spatial Predictive Model in Arid Utah." DigitalCommons@USU, 2010. https://digitalcommons.usu.edu/etd/736.
Full textKalmár, Marcus, and 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.
Full textWagner, 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.
Full textZhang, Yi. "Strategies for Combining Tree-Based Ensemble Models." NSUWorks, 2017. http://nsuworks.nova.edu/gscis_etd/1021.
Full textJonsson, Estrid, and 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.
Full textAs 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.
Full textVictors, Mason Lemoyne. "A Classification Tool for Predictive Data Analysis in Healthcare." BYU ScholarsArchive, 2013. https://scholarsarchive.byu.edu/etd/5639.
Full textOspina, Arango Juan David. "Predictive models for side effects following radiotherapy for prostate cancer." Thesis, Rennes 1, 2014. http://www.theses.fr/2014REN1S046/document.
Full textExternal 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.
Full textBooks on the topic "Random Forest predictive model"
Ravi, Margasahayam, and John F. Kennedy Space Center., eds. Validation of a deterministic vibroacoustic response prediction model. Kennedy Space Center, Fla: National Aeronautics and Space Administration, John F. Kennedy Space Center, 1997.
Find full textRavi, Margasahayam, and John F. Kennedy Space Center., eds. Validation of a deterministic vibroacoustic response prediction model. Kennedy Space Center, Fla: National Aeronautics and Space Administration, John F. Kennedy Space Center, 1997.
Find full textLópez, 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.
Find full textTechnische Zuverlässigkeit 2021. VDI Verlag, 2021. http://dx.doi.org/10.51202/9783181023778.
Full textAnderson, Raymond A. Credit Intelligence & Modelling. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780192844194.001.0001.
Full textFrey, Ulrich. Sustainable Governance of Natural Resources. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780197502211.001.0001.
Full textBook chapters on the topic "Random Forest predictive model"
Al-Quraishi, Tahsien, Jemal H. Abawajy, Morshed U. Chowdhury, Sutharshan Rajasegarar, and 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.
Full textVaiciukynaite, Egle, Ineta Zickute, and 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.
Full textKhandelwal, Veena, and 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.
Full textDhamodaran, S., Ch Krishna Chaitanya Varma, and 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.
Full textWu, Jimmy Ming-Tai, Meng-Hsiun Tsai, Sheng-Han Xiao, and 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.
Full textSong, Wanchao, and 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.
Full textPrasath, N., J. Sreemathy, N. Krishnaraj, and 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.
Full textLiu, Siqi, Hao Du, and 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.
Full textXue, Ruixiang, and 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.
Full textZhao, Zhijie, Wanting Zhou, Zeguo Qiu, Ang Li, and 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.
Full textConference papers on the topic "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.
Full textZhu, Lin, Jiaxing Lu, and 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.
Full textRamadevi, R., V. J. K. Kishoresonti, M. Jain Jacob, V. Vaidehi, N. Mohankumar, and 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.
Full textS. Pahl, Eric, W. Nick Street, Hans J. Johnson, and 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.
Full textZhang, Zhidong, Xiubin Zhu, and 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.
Full textJoshi, Shreya, Rachana Ramesh, and 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.
Full textRaut, Archana, Dipti Theng, and 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.
Full textWang, Danqin, and 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.
Full textMallahi, Imad El, Asmae Dlia, Jamal Riffi, Mohamed Adnane Mahraz, and 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.
Full textWen, Zhang, Zhaorui Jiang, and 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.
Full textReports on the topic "Random Forest predictive model"
Ogunbire, Abimbola, Panick Kalambay, Hardik Gajera, and Srinivas Pulugurtha. Deep Learning, Machine Learning, or Statistical Models for Weather-related Crash Severity Prediction. Mineta Transportation Institute, December 2023. http://dx.doi.org/10.31979/mti.2023.2320.
Full textZyphur, Michael. Dynamic Structural Equation Modeling in Mplus. Instats Inc., 2023. http://dx.doi.org/10.61700/aypvl8azm5nlr469.
Full textLiu, Hongrui, and 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.
Full textMeidani, Hadi, and 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.
Full textPompeu, Gustavo, and 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.
Full textWhite, Michael J., and 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.
Full textLi, Yuan, Benjamin Metcalf, Sopio Chochua, Zhongya Li, Robert Gertz, Hollis Walker, Paulina Hawkins, Theresa Tran, Lesley McGee, and 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.
Full textPuttanapong, Nattapong, Arturo M. Martinez Jr, Mildred Addawe, Joseph Bulan, Ron Lester Durante, and Marymell Martillan. Predicting Poverty Using Geospatial Data in Thailand. Asian Development Bank, December 2020. http://dx.doi.org/10.22617/wps200434-2.
Full textRossi, Jose Luiz, Carlos Piccioni, Marina Rossi, and Daniel Cuajeiro. Brazilian Exchange Rate Forecasting in High Frequency. Inter-American Development Bank, September 2022. http://dx.doi.org/10.18235/0004488.
Full textVas, Dragos, Elizabeth Corriveau, Lindsay Gaimaro, and 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.), December 2023. http://dx.doi.org/10.21079/11681/48018.
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