Gotowe bibliografie tematyczne / Random Forest predictive model

Gotowa bibliografia na temat „Random Forest predictive model”

Autor: Grafiati

Data publikacji: 7 lipca 2024

Data aktualizacji: 7 lipca 2024

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Artykuły w czasopismach na temat "Random Forest predictive model"

Mohnen, Sigrid M., Adriënne H. Rotteveel, Gerda Doornbos i 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.

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AbstractWe investigated the additional predictive value of an individual’s neighbourhood (quality and location), and of changes therein on his/her healthcare costs. To this end, we combined several Dutch nationwide data sources from 2003 to 2014, and selected inhabitants who moved in 2010. We used random forest models to predict the area under the curve of the regular healthcare costs of individuals in the years 2011–2014. In our analyses, the quality of the neighbourhood before the move appeared to be quite important in predicting healthcare costs (i.e. importance rank 11 out of 126 socio-demographic and neighbourhood variables; rank 73 out of 261 in the full model with prior expenditure and medication). The predictive performance of the models was evaluated in terms of R2 (or proportion of explained variance) and MAE (mean absolute (prediction) error). The model containing only socio-demographic information improved marginally when neighbourhood was added (R2 +0.8%, MAE −€5). The full model remained the same for the study population (R2 = 48.8%, MAE of €1556) and for subpopulations. These results indicate that only in prediction models in which prior expenditure and utilization cannot or ought not to be used neighbourhood might be an interesting source of information to improve predictive performance.

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Wang, Fangyi, Yongchao Wang, Xiaokang Ji i 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.

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(1) Background: Macrosomia is prevalent in China and worldwide. The current method of predicting macrosomia is ultrasonography. We aimed to develop new predictive models for recognizing macrosomia using a random forest model to improve the sensitivity and specificity of macrosomia prediction; (2) Methods: Based on the Shandong Multi-Center Healthcare Big Data Platform, we collected the prenatal examination and delivery data from June 2017 to May 2018 in Jinan, including the macrosomia and normal-weight newborns. We constructed a random forest model and a logistic regression model for predicting macrosomia. We compared the validity and predictive value of these two methods and the traditional method; (3) Results: 405 macrosomia cases and 3855 normal-weight newborns fit the selection criteria and 405 pairs of macrosomia and control cases were brought into the random forest model and logistic regression model. On the basis of the average decrease of the Gini coefficient, the order of influencing factors was: interspinal diameter, transverse outlet, intercristal diameter, sacral external diameter, pre-pregnancy body mass index, age, the number of pregnancies, and the parity. The sensitivity, specificity, and area under curve were 91.7%, 91.7%, and 95.3% for the random forest model, and 56.2%, 82.6%, and 72.0% for logistic regression model, respectively; the sensitivity and specificity were 29.6% and 97.5% for the ultrasound; (4) Conclusions: A random forest model based on the maternal information can be used to predict macrosomia accurately during pregnancy, which provides a scientific basis for developing rapid screening and diagnosis tools for macrosomia.

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Kor, 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.

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Global solar radiation estimation is crucial for regional climate assessment and crop growth. Therefore, studies on the prediction of solar radiation are emerging. With the availability of the public data on solar radiation, computerized models have been developed as well. These predictive models play significant role in determining the potentials of regions suitable for renewable energy generation required by engineering and agricultural activities. Herein a computerized model has been presented for estimating global solar radiation. The model utilizes random forest algorithm and reached predictive value of 93.9%.

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Rigatti, Steven J. "Random Forest". Journal of Insurance Medicine 47, nr 1 (1.01.2017): 31–39. http://dx.doi.org/10.17849/insm-47-01-31-39.1.

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For the task of analyzing survival data to derive risk factors associated with mortality, physicians, researchers, and biostatisticians have typically relied on certain types of regression techniques, most notably the Cox model. With the advent of more widely distributed computing power, methods which require more complex mathematics have become increasingly common. Particularly in this era of “big data” and machine learning, survival analysis has become methodologically broader. This paper aims to explore one technique known as Random Forest. The Random Forest technique is a regression tree technique which uses bootstrap aggregation and randomization of predictors to achieve a high degree of predictive accuracy. The various input parameters of the random forest are explored. Colon cancer data (n = 66,807) from the SEER database is then used to construct both a Cox model and a random forest model to determine how well the models perform on the same data. Both models perform well, achieving a concordance error rate of approximately 18%.

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Wei, Li-Li, Yue-Shuai Pan, Yan Zhang, Kai Chen, Hao-Yu Wang i Jing-Yuan Wang. "Application of machine learning algorithm for predicting gestational diabetes mellitus in early pregnancy†". Frontiers of Nursing 8, nr 3 (1.09.2021): 209–21. http://dx.doi.org/10.2478/fon-2021-0022.

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Abstract Objective To study the application of a machine learning algorithm for predicting gestational diabetes mellitus (GDM) in early pregnancy. Methods This study identified indicators related to GDM through a literature review and expert discussion. Pregnant women who had attended medical institutions for an antenatal examination from November 2017 to August 2018 were selected for analysis, and the collected indicators were retrospectively analyzed. Based on Python, the indicators were classified and modeled using a random forest regression algorithm, and the performance of the prediction model was analyzed. Results We obtained 4806 analyzable data from 1625 pregnant women. Among these, 3265 samples with all 67 indicators were used to establish data set F1; 4806 samples with 38 identical indicators were used to establish data set F2. Each of F1 and F2 was used for training the random forest algorithm. The overall predictive accuracy of the F1 model was 93.10%, area under the receiver operating characteristic curve (AUC) was 0.66, and the predictive accuracy of GDM-positive cases was 37.10%. The corresponding values for the F2 model were 88.70%, 0.87, and 79.44%. The results thus showed that the F2 prediction model performed better than the F1 model. To explore the impact of sacrificial indicators on GDM prediction, the F3 data set was established using 3265 samples (F1) with 38 indicators (F2). After training, the overall predictive accuracy of the F3 model was 91.60%, AUC was 0.58, and the predictive accuracy of positive cases was 15.85%. Conclusions In this study, a model for predicting GDM with several input variables (e.g., physical examination, past history, personal history, family history, and laboratory indicators) was established using a random forest regression algorithm. The trained prediction model exhibited a good performance and is valuable as a reference for predicting GDM in women at an early stage of pregnancy. In addition, there are certain requirements for the proportions of negative and positive cases in sample data sets when the random forest algorithm is applied to the early prediction of GDM.

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Diaz, Pablo, Juan C. Salas, Aldo Cipriano i Felipe Núñez. "Random forest model predictive control for paste thickening". Minerals Engineering 163 (marzec 2021): 106760. http://dx.doi.org/10.1016/j.mineng.2020.106760.

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Mao, Yiwen, i Asgeir Sorteberg. "Improving Radar-Based Precipitation Nowcasts with Machine Learning Using an Approach Based on Random Forest". Weather and Forecasting 35, nr 6 (grudzień 2020): 2461–78. http://dx.doi.org/10.1175/waf-d-20-0080.1.

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AbstractA binary classification model is trained by random forest using data from 41 stations in Norway to predict the precipitation in a given hour. The predictors consist of results from radar nowcasts and numerical weather predictions. The results demonstrate that the random forest model can improve the precipitation predictions by the radar nowcasts and the numerical weather predictions. This study clarifies whether certain potential factors related to model training can influence the predictive skill of the random forest method. The results indicate that enforcing a balanced prediction by resampling the training datasets or lowering the threshold probability for classification cannot improve the predictive skill of the random forest model. The study reveals that the predictive skill of the random forest model shows seasonality, but is only weakly influenced by the geographic diversity of the training dataset. Finally, the study shows that the most important predictor is the precipitation predictions by the radar nowcasts followed by the precipitation predictions by the numerical weather predictions. Although meteorological variables other than precipitation are weaker predictors, the results suggest that they can help to reduce the false alarm ratio and to increase the success ratio of the precipitation prediction.

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Bashir Suleiman, Aminu, Stephen Luka i 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.

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Every year, cardiovascular disease (CVD) claims the lives of nearly 17 million people worldwide. Predicting heart disease early and accurately can help delay therapies and improve results. Patient data analysis machine learning techniques have shown promise for better predictive capabilities than conventional methods; however, there are still gaps in areas such as algorithm blending, standardization, feature optimization, and model tuning that require strong methodology. By benchmarking against established methods, this study attempts to create a more sophisticated machine learning model with detailed performance and a robust approach for predicting heart disease. Using a clinical dataset that was obtained from an internet repository, an improved random forest (RF) model was created. It was then tested against baseline logistic regression and support vector machine models, Naïve Bayes Classifier, K Nearest Neighbors Classifier, and Decision Tree Classifier. RF hyperparameter tweaking, redundant feature filtering, and systematic data preprocessing were used. Accuracy, precision, recall, F1 score, and ROC analysis were computed as evaluation measures. With F1 score, 1.00 AUC, and 90% accuracy, The RF model demonstrated superior performance compared to the remaining models, which exhibited, AUCs of 0.9, 0.82, and 0.9. On the public dataset, the refined RF model demonstrated exceptional predictive performance, highlighting the promise of a methodical machine learning approach to improve heart disease prediction. The external clinical validation and optimization of various patient populations should be the main areas of attention for future research.

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Jeong, Hoyeon, Youngjune Kim i 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.

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This study contributes to guidance for understanding farmland purchase and rent decisions in Korea via an analysis using a machine learning tool, Random Forests: A Supervised Machine Learning Algorithm. Farm Household Economy Survey is employed to predict the relationship between farmland acquisition and farm household economic characteristics. Our main findings are two folds. First, a farmland purchase decision is positively related to transfer incomes, the value of inventory & fixed assets, and the value of farmland that farmers owned. Second, a farmland rent decision is also positively associated with a rent paid in a prior year, revenue from field crops, inventory and agricultural assets, and transfer incomes.

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Emir, Senol, Hasan Dincer, Umit Hacioglu i 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.

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The purpose of this study is to explore the importance and ranking of technical analysis variables in Turkish banking sector. Random Forest method is used for determining importance scores of inputs for eight banks in Borsa Istanbul. Then two predictive models utilizing Random Forest (RF) and Artificial Neural Networks (ANN) are built for predicting BIST-100 index and bank closing prices. Results of the models are compared by three metrics namely Mean Absolute Error (MAE), Mean Square Error (MSE), Median Absolute Error (MedAE). Findings show that moving average (MAV-100) is the most important variable for both BIST -100 index and bank closing prices. Therefore, investors should follow this technical indicator with respect to Turkish banks. In addition ANN shows better performance for all metrics.

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Rozprawy doktorskie na temat "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.

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This thesis is concerned with developing methodologies that enable existing models to be effectively reused. Results of this thesis are presented in the framework of Quantitative Structural-Activity Relationship (QSAR) models, but their application is much more general. QSAR models relate chemical structures with their biological, chemical or environmental activity. There are many applications that offer an environment to build and store predictive models. Unfortunately, they do not provide advanced functionalities that allow for efficient model selection and for interpretation of model predictions for new data. This thesis aims to address these issues and proposes methodologies for dealing with three research problems: model governance (management), model identification (selection), and interpretation of model predictions. The combination of these methodologies can be employed to build more efficient systems for model reuse in QSAR modelling and other areas. The first part of this study investigates toxicity data and model formats and reviews some of the existing toxicity systems in the context of model development and reuse. Based on the findings of this review and the principles of data governance, a novel concept of model governance is defined. Model governance comprises model representation and model governance processes. These processes are designed and presented in the context of model management. As an application, minimum information requirements and an XML representation for QSAR models are proposed. Once a collection of validated, accepted and well annotated models is available within a model governance framework, they can be applied for new data. It may happen that there is more than one model available for the same endpoint. Which one to chose? The second part of this thesis proposes a theoretical framework and algorithms that enable automated identification of the most reliable model for new data from the collection of existing models. The main idea is based on partitioning of the search space into groups and assigning a single model to each group. The construction of this partitioning is difficult because it is a bi-criteria problem. The main contribution in this part is the application of Pareto points for the search space partition. The proposed methodology is applied to three endpoints in chemoinformatics and predictive toxicology. After having identified a model for the new data, we would like to know how the model obtained its prediction and how trustworthy it is. An interpretation of model predictions is straightforward for linear models thanks to the availability of model parameters and their statistical significance. For non linear models this information can be hidden inside the model structure. This thesis proposes an approach for interpretation of a random forest classification model. This approach allows for the determination of the influence (called feature contribution) of each variable on the model prediction for an individual data. In this part, there are three methods proposed that allow analysis of feature contributions. Such analysis might lead to the discovery of new patterns that represent a standard behaviour of the model and allow additional assessment of the model reliability for new data. The application of these methods to two standard benchmark datasets from the UCI machine learning repository shows a great potential of this methodology. The algorithm for calculating feature contributions has been implemented and is available as an R package called rfFC.

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Stum, Alexander Knell. "Random Forests Applied as a Soil Spatial Predictive Model in Arid Utah". DigitalCommons@USU, 2010. https://digitalcommons.usu.edu/etd/736.

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Initial soil surveys are incomplete for large tracts of public land in the western USA. Digital soil mapping offers a quantitative approach as an alternative to traditional soil mapping. I sought to predict soil classes across an arid to semiarid watershed of western Utah by applying random forests (RF) and using environmental covariates derived from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and digital elevation models (DEM). Random forests are similar to classification and regression trees (CART). However, RF is doubly random. Many (e.g., 500) weak trees are grown (trained) independently because each tree is trained with a new randomly selected bootstrap sample, and a random subset of variables is used to split each node. To train and validate the RF trees, 561 soil descriptions were made in the field. An additional 111 points were added by case-based reasoning using aerial photo interpretation. As RF makes classification decisions from the mode of many independently grown trees, model uncertainty can be derived. The overall out of the bag (OOB) error was lower without weighting of classes; weighting increased the overall OOB error and the resulting output did not reflect soil-landscape relationships observed in the field. The final RF model had an OOB error of 55.2% and predicted soils on landforms consistent with soil-landscape relationships. The OOB error for individual classes typically decreased with increasing class size. In addition to the final classification, I determined the second and third most likely classification, model confidence, and the hypothetical extent of individual classes. Pixels that had high possibility of belonging to multiple soil classes were aggregated using a minimum confidence value based on limiting soil features, which is an effective and objective method of determining membership in soil map unit associations and complexes mapped at the 1:24,000 scale. Variables derived from both DEM and Landsat 7 ETM+ sources were important for predicting soil classes based on Gini and standard measures of variable importance and OOB errors from groves grown with exclusively DEM- or Landsat-derived data. Random forests was a powerful predictor of soil classes and produced outputs that facilitated further understanding of soil-landscape relationships.

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Kalmár, Marcus, i 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.

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Forecasting is used for decision making and unreliable predictions can instill a false sense of condence. Traditional time series modelling is astatistical art form rather than a science and errors can occur due to lim-itations of human judgment. In minimizing the risk of falsely specifyinga process the practitioner can make use of machine learning models. Inan eort to nd out if there's a benet in using models that require lesshuman judgment, the machine learning models Random Forest and Neural Network have been used to model a VAR(1) time series. In addition,the classical time series models AR(1), AR(2), VAR(1) and VAR(2) havebeen used as comparative foundation. The Random Forest and NeuralNetwork are trained and ultimately the models are used to make pre-dictions evaluated by RMSE. All models yield scattered forecast resultsexcept for the Random Forest that steadily yields comparatively precisepredictions. The study shows that there is denitive benet in using Random Forests to eliminate the risk of falsely specifying a process and do infact provide better results than a correctly specied model.

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Wagner, 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.

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Zhang, Yi. "Strategies for Combining Tree-Based Ensemble Models". NSUWorks, 2017. http://nsuworks.nova.edu/gscis_etd/1021.

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Ensemble models have proved effective in a variety of classification tasks. These models combine the predictions of several base models to achieve higher out-of-sample classification accuracy than the base models. Base models are typically trained using different subsets of training examples and input features. Ensemble classifiers are particularly effective when their constituent base models are diverse in terms of their prediction accuracy in different regions of the feature space. This dissertation investigated methods for combining ensemble models, treating them as base models. The goal is to develop a strategy for combining ensemble classifiers that results in higher classification accuracy than the constituent ensemble models. Three of the best performing tree-based ensemble methods – random forest, extremely randomized tree, and eXtreme gradient boosting model – were used to generate a set of base models. Outputs from classifiers generated by these methods were then combined to create an ensemble classifier. This dissertation systematically investigated methods for (1) selecting a set of diverse base models, and (2) combining the selected base models. The methods were evaluated using public domain data sets which have been extensively used for benchmarking classification models. The research established that applying random forest as the final ensemble method to integrate selected base models and factor scores of multiple correspondence analysis turned out to be the best ensemble approach.

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Jonsson, Estrid, i 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.

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Allt eftersom klimatkrisen fortskrider ökar engagemanget kring hållbarhet inom företag. Växthusgaser är ett av de största problemen och matsvinn har därför fått mycket uppmärksamhet sedan det utnämndes till den tredje största bidragaren till de globala utsläppen. För att minska sitt bidrag rabatterar många matbutiker produkter med kort bästföredatum, vilket kommit att kräva en förståelse för hur priskänslig efterfrågan på denna typ av produkt är. Prisoptimering görs vanligtvis med så kallade Generalized Linear Models men då efterfrågan är ett komplext koncept har maskininl ärningsmetoder börjat utmana de traditionella modellerna. En sådan metod är Random Forest Regression, och syftet med uppsatsen är att utreda ifall modellen är bättre på att estimera efterfrågan baserat på rabattnivå än en klassisk linjär modell. Vidare utreds det ifall ett tydligt linjärt samband existerar mellan rabattnivå och efterfrågan, samt ifall detta beror av produkttyp. Resultaten visar på att Random Forest tar bättre hänsyn till det komplexa samband som visade sig finnas, och i detta specifika fall presterar bättre. Vidare visade resultaten att det sammantaget inte finns något linjärt samband, men att vissa produktkategorier uppvisar svag linjäritet.
As 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.

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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.

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Friction stir welding is an advanced welding process that is being investigated for use in many different industries. One area that has been investigated for its application is in healing critical nuclear reactor components that are developing cracks. However, friction stir welding is a complicated process and it is difficult to predict what the final properties of a set of welding parameters will be. This thesis sets forth a method using finite element analysis and a random forest model to accurately predict hardness in the welding nugget after processing. The finite element analysis code used and ALE formulation that enabled an Eulerian approach to modeling. Hardness is used as the property to estimate because of its relationship to tensile strength and grain size. The input parameters to the random forest model are temperature, cooling rate, strain rate, and RPM. Two welding parameter sets were used to train the model. The method was found to have a high level of accuracy as measured by R^2, but had greater difficulty in predicting the parameter set with higher RPM.

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Victors, Mason Lemoyne. "A Classification Tool for Predictive Data Analysis in Healthcare". BYU ScholarsArchive, 2013. https://scholarsarchive.byu.edu/etd/5639.

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Hidden Markov Models (HMMs) have seen widespread use in a variety of applications ranging from speech recognition to gene prediction. While developed over forty years ago, they remain a standard tool for sequential data analysis. More recently, Latent Dirichlet Allocation (LDA) was developed and soon gained widespread popularity as a powerful topic analysis tool for text corpora. We thoroughly develop LDA and a generalization of HMMs and demonstrate the conjunctive use of both methods in predictive data analysis for health care problems. While these two tools (LDA and HMM) have been used in conjunction previously, we use LDA in a new way to reduce the dimensionality involved in the training of HMMs. With both LDA and our extension of HMM, we train classifiers to predict development of Chronic Kidney Disease (CKD) in the near future.

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Ospina, Arango Juan David. "Predictive models for side effects following radiotherapy for prostate cancer". Thesis, Rennes 1, 2014. http://www.theses.fr/2014REN1S046/document.

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La radiothérapie externe (EBRT en anglais pour External Beam Radiotherapy) est l'un des traitements référence du cancer de prostate. Les objectifs de la radiothérapie sont, premièrement, de délivrer une haute dose de radiations dans la cible tumorale (prostate et vésicules séminales) afin d'assurer un contrôle local de la maladie et, deuxièmement, d'épargner les organes à risque voisins (principalement le rectum et la vessie) afin de limiter les effets secondaires. Des modèles de probabilité de complication des tissus sains (NTCP en anglais pour Normal Tissue Complication Probability) sont nécessaires pour estimer sur les risques de présenter des effets secondaires au traitement. Dans le contexte de la radiothérapie externe, les objectifs de cette thèse étaient d'identifier des paramètres prédictifs de complications rectales et vésicales secondaires au traitement; de développer de nouveaux modèles NTCP permettant l'intégration de paramètres dosimétriques et de paramètres propres aux patients; de comparer les capacités prédictives de ces nouveaux modèles à celles des modèles classiques et de développer de nouvelles méthodologies d'identification de motifs de dose corrélés à l'apparition de complications. Une importante base de données de patients traités par radiothérapie conformationnelle, construite à partir de plusieurs études cliniques prospectives françaises, a été utilisée pour ces travaux. Dans un premier temps, la fréquence des symptômes gastro-Intestinaux et génito-Urinaires a été décrite par une estimation non paramétrique de Kaplan-Meier. Des prédicteurs de complications gastro-Intestinales et génito-Urinaires ont été identifiés via une autre approche classique : la régression logistique. Les modèles de régression logistique ont ensuite été utilisés dans la construction de nomogrammes, outils graphiques permettant aux cliniciens d'évaluer rapidement le risque de complication associé à un traitement et d'informer les patients. Nous avons proposé l'utilisation de la méthode d'apprentissage de machine des forêts aléatoires (RF en anglais pour Random Forests) pour estimer le risque de complications. Les performances de ce modèle incluant des paramètres cliniques et patients, surpassent celles des modèle NTCP de Lyman-Kutcher-Burman (LKB) et de la régression logistique. Enfin, la dose 3D a été étudiée. Une méthode de décomposition en valeurs populationnelles (PVD en anglais pour Population Value Decomposition) en 2D a été généralisée au cas tensoriel et appliquée à l'analyse d'image 3D. L'application de cette méthode à une analyse de population a été menée afin d'extraire un motif de dose corrélée à l'apparition de complication après EBRT. Nous avons également développé un modèle non paramétrique d'effets mixtes spatio-Temporels pour l'analyse de population d'images tridimensionnelles afin d'identifier une région anatomique dans laquelle la dose pourrait être corrélée à l'apparition d'effets secondaires
External 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

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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.

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Essential genes are those whose presence is imperative for an organism's survival, whereas the functions of non-essential genes may be useful but not critical. Abnormal functionality of essential genes may lead to defects or death at an early stage of life. Knowledge of essential genes is therefore key to understanding development, maintenance of major cellular processes and tissue-specific functions that are crucial for life. Existing experimental techniques for identifying essential genes are accurate, but most of them are time consuming and expensive. Predicting essential genes using computational methods, therefore, would be of great value as they circumvent experimental constraints. Our research is based on the hypothesis that mammalian essential (lethal) and non-essential (viable) genes are distinguishable by various properties. We examined a wide range of features of Mus musculus genes, including sequence, protein-protein interactions, gene expression and function, and found 75 features that were statistically discriminative between lethal and viable genes. These features were used as inputs to create a novel machine learning classifier, allowing the prediction of a mouse gene as lethal or viable with the cross-validation and blind test accuracies of ∼91% and ∼93%, respectively. The prediction results are promising, indicating that our classifier is an effective mammalian essential gene prediction method. We further developed the mouse gene essentiality study by analysing the association between essentiality and gene duplication. Mouse genes were labelled as singletons or duplicates, and their expression patterns over 13 developmental stages were examined. We found that lethal genes originating from duplicates are considerably lower in proportion than singletons. At all developmental stages a significantly higher proportion of singletons and lethal genes are expressed than duplicates and viable genes. Lethal genes were also found to be more ancient than viable genes. In addition, we observed that duplicate pairs with similar patterns of developmental co-expression are more likely to be viable; lethal gene duplicate pairs do not have such a trend. Overall, these results suggest that duplicate genes in mouse are less likely to be essential than singletons. Finally, we investigated the evolutionary age of mouse genes across development to see if the morphological hourglass pattern exists in the mouse. We found that in mouse embryos, genes expressed in early and late stages are evolutionarily younger than those expressed in mid-embryogenesis, thus yielding an hourglass pattern. However, the oldest genes are not expressed at the phylotypic stage stated in prior studies, but instead at an earlier time point - the egg cylinder stage. These results question the application of the hourglass model to mouse development.

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Książki na temat "Random Forest predictive model"

Ravi, Margasahayam, i John F. Kennedy Space Center., red. Validation of a deterministic vibroacoustic response prediction model. Kennedy Space Center, Fla: National Aeronautics and Space Administration, John F. Kennedy Space Center, 1997.

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López, 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.

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Technische Zuverlässigkeit 2021. VDI Verlag, 2021. http://dx.doi.org/10.51202/9783181023778.

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Aus dem Vorwort: Durch die zunehmende Digitalisierung und Vernetzung, beispielsweise in einer Smart Factory im Kontext von Industrie 4.0, werden hohe Anforderungen an die Zuverlässigkeit, die Verfügbarkeit und die Sicherheit von Maschinen und Anlagen gestellt. Dies erfordert den konsequenten Einsatz und die ständige Weiterentwicklung von Methoden und Modellen der Zuverlässigkeitstechnik entlang des gesamten Lebenszyklus zur Planung, Entwicklung und Absicherung der Zuverlässigkeit. Die zunehmende Digitalisierung bietet durch die steigende Zugänglichkeit und Verfügbarkeit von relevanten Daten gleichzeitig enorme Chancen und neue Möglichkeiten für die Anwendung dieser Methoden und Modelle für Zuverlässigkeitsanalysen und -prognosen. Inhalt Prognostics and Health Management (PHM) und Industrie 4.0 Restlebensdauervorhersage für Filtrationssysteme mittels Random Forest ..... 3 Untersuchung von Datensätzen und Definition praxisrelevanter Standardfälle im Kontext von Predictive Maintenance ..... 17 Methodik zur Schadensquantifizierung in hydraulischen Axialkolbeneinheiten unter variablen Betriebsbedingungen ..... 33 ...

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Anderson, Raymond A. Credit Intelligence & Modelling. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780192844194.001.0001.

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This book, “Forest Paths” for short, started as a detailed guide for the construction of predictive models for credit and other risk assessment, for use in big-bank retail lending. It became a textbook covering credit processes (from marketing through to fraud), bureau and rating agencies, and various tools. Included are detailed histories (economics, statistics, social science}, which much referencing. It is unique in the field, with chatpers’-end questions. The primary target market is corporate and academic, but much would be of interest to a broader audience. There are eight modules: 1) an introduction to credit risk assessment and predictive modelling; 2) micro-histories of credit, credit intelligence, credit scoring, plus industrial revolutions, economic ups and downs, and both personal registration and identification; 4) mathematical and statistical tools used to develop and assess predictive models; 5) project management and data assembly; 6) data preparation from sampling to reject inference; 7) model training through to implementation; and 8) appendices, including an extensive glossary, bibliography, and index. Although the focus is credit risk, especially in the retail consumer and small-business segments, many concepts are common across disciplines as diverse as psychology, biology, engineering, and computer science, whether academic research or practical use. It also covers issues relating to the use of machine learning for credit risk assessment. Most of the focus is on traditional modelling techniques, but the increasing use of machine learning is recognised, as are its limitations. It is hoped that the contents will inform both camps.

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Frey, Ulrich. Sustainable Governance of Natural Resources. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780197502211.001.0001.

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Natural resources are often overexploited. Nevertheless, there are counterexamples of sustainably using common-pool resources. This book analyses the most important factors influencing the management of natural resources. Hence, the important question—What makes some systems successful?—is answered in this book. Based on three of the world’s largest data sets on fisheries, forest management, and irrigation systems, success factors are empirically examined. The book presents a synthesis of twenty-four success factors that explain ecological success, such as participation possibilities. The analysis in this book uses a range of statistical and machine learning methods to develop highly predictive, robust, and empirically sound models that shed new light on factors that have already been investigated. From this analysis the author develops a general model which can predict the success of in natural resource management very well, depending on the identified success factors.

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Części książek na temat "Random Forest predictive model"

Al-Quraishi, Tahsien, Jemal H. Abawajy, Morshed U. Chowdhury, Sutharshan Rajasegarar i Ahmad Shaker Abdalrada. "Breast Cancer Recurrence Prediction Using Random Forest Model". W 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.

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Vaiciukynaite, Egle, Ineta Zickute i Justas Salkevicius. "Solutions of Brand Posts on Facebook to Increase Customer Engagement Using the Random Forest Prediction Model". W 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.

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AbstractThis paper aims to predict customer engagement behaviour (CEB), i.e. likes, shares, comments, and emoji reactions, on company posts on Facebook. A sample of 1109 brand posts from Facebook pages in Lithuania was used. The Random Forest method was used to train models to predict customer engagement behaviour based on features including time frame, content, and media types of brand posts. The data was used for training nine binary classification models using the Random Forest method, which can predict the popularity of a company’s posts. In terms of social score, accuracy of likes, comments, and shares varied from 68.4% (likes on a post) to 84.0% (comments on a post). For emotional responses, accuracy varied from 65.6% (‘wow’ on a post) to 82.5% (‘ha ha’ on a post). The data was collected from one single media platform and country, and encompassed emotional expressions at an early stage on Facebook. The findings of Random Forest prediction models can help organisations to make more efficient solutions for brand posts on Facebook to increase customer engagement. This paper outlines the first steps in creating a predictive engagement score towards diverse types of brand posts on Facebook. The same approach to features of brand posts might be applied to other social media platforms such as Instagram and LinkedIn.

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Khandelwal, Veena, i Shantanu Khandelwal. "Ground Water Quality Index Prediction Using Random Forest Model". W 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.

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Dhamodaran, S., Ch Krishna Chaitanya Varma i Chittepu Dwarakanath Reddy. "Weather Prediction Model Using Random Forest Algorithm and GIS Data Model". W 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.

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Wu, Jimmy Ming-Tai, Meng-Hsiun Tsai, Sheng-Han Xiao i Tsu-Yang Wu. "Construct Left Ventricular Hypertrophy Prediction Model Based on Random Forest". W 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.

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Song, Wanchao, i Yinghua Zhou. "Road Travel Time Prediction Method Based on Random Forest Model". W Smart Innovation, Systems and Technologies, 155–63. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0077-0_17.

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Prasath, N., J. Sreemathy, N. Krishnaraj i P. Vigneshwaran. "Analysis of Crop Yield Prediction Using Random Forest Regression Model". W Smart Innovation, Systems and Technologies, 239–49. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-7447-2_22.

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Liu, Siqi, Hao Du i Mengling Feng. "Robust Predictive Models in Clinical Data—Random Forest and Support Vector Machines". W 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.

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Xue, Ruixiang, i Hua Ding. "Risk Prediction of Corporate Earnings Manipulation Based on Random Forest Model". W 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.

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Zhao, Zhijie, Wanting Zhou, Zeguo Qiu, Ang Li i Jiaying Wang. "Research on Ctrip Customer Churn Prediction Model Based on Random Forest". W Business Intelligence and Information Technology, 511–23. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92632-8_48.

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Streszczenia konferencji na temat "Random Forest predictive model"

Adeeyo, Yisa. "Random Forest Ensemble Model for Reservoir Fluid Property Prediction". W SPE Nigeria Annual International Conference and Exhibition. SPE, 2022. http://dx.doi.org/10.2118/212044-ms.

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Abstract Reservoir fluid PVT properties are measured in the laboratory for various use in reservoir engineering evaluation and estimation. Despite the indispensability of these PVT parameters, PVT lab data are seldomly available and if available may be unreliable. Instead, various empirical models have been developed and used in the industry. These empirical models are inherently inaccurate when used to predict PVT properties of fluid from different geological region with different depositional environment and fingerprint. Artificial Intelligence (AI) has evolved over the years and provided some algorithms with potentials to develop accurate predictive model for the prediction of bubblepoint pressure. This work tested some AI algorithms, compared performances and choose random forest regression algorithm in developing a robust predictive model for the estimation of bubblepoint pressure. Two thousand five hundred and twenty-two datasets obtained from oil reservoirs in different geographical locations were used for the feature scaling of input data, training and testing of the models. The independent variables, gas-oil ratio, temperature, oil density and gas density were confirmed to have key influence on the dependent variable Bubblepoint pressure The random forest model developed uses ensemble learning approach, combines predictions from multiple machine learning algorithms by averaging all predictions to make a more accurate prediction. The ‘forest’ generated by the random forest algorithm was trained through bootstrap aggregating. This is an ensemble meta-algorithm that improves the accuracy of machine learning algorithms. Percentage data split was 70% training and 30% testing. The reliability, accuracy and completeness of the predictive model capability were computed through performance indices such as the root mean square error (RMSE) and mean absolute error (MAE). The best network architecture was determined along with the corresponding test set RMSE, and Correlation coefficient. Statistical and graphical error analysis of the results showed that the random forest model performed better than existing models with 0.98 correlation coefficients for bubblepoint pressure. Better accuracy of reservoir properties prediction could be achieved using this random forest reservoir fluid properties prediction model.

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Zhu, Lin, Jiaxing Lu i Yihong Chen. "HDI-Forest: Highest Density Interval Regression Forest". W 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.

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By seeking the narrowest prediction intervals (PIs) that satisfy the specified coverage probability requirements, the recently proposed quality-based PI learning principle can extract high-quality PIs that better summarize the predictive certainty in regression tasks, and has been widely applied to solve many practical problems. Currently, the state-of-the-art quality-based PI estimation methods are based on deep neural networks or linear models. In this paper, we propose Highest Density Interval Regression Forest (HDI-Forest), a novel quality-based PI estimation method that is instead based on Random Forest. HDI-Forest does not require additional model training, and directly reuses the trees learned in a standard Random Forest model. By utilizing the special properties of Random Forest, HDI-Forest could efficiently and more directly optimize the PI quality metrics. Extensive experiments on benchmark datasets show that HDI-Forest significantly outperforms previous approaches, reducing the average PI width by over 20% while achieving the same or better coverage probability.

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Ramadevi, R., V. J. K. Kishoresonti, M. Jain Jacob, V. Vaidehi, N. Mohankumar i M. Rajmohan. "Random Forest Predictive Model for Ventilator-Associated Pneumonia Prediction with IoT Data Analytics". W 2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS). IEEE, 2024. http://dx.doi.org/10.1109/adics58448.2024.10533652.

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S. Pahl, Eric, W. Nick Street, Hans J. Johnson i Alan I. Reed. "A Predictive Model for Kidney Transplant Graft Survival using Machine Learning". W 4th International Conference on Computer Science and Information Technology (COMIT 2020). AIRCC Publishing Corporation, 2020. http://dx.doi.org/10.5121/csit.2020.101609.

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Kidney transplantation is the best treatment for end-stage renal failure patients. The predominant method used for kidney quality assessment is the Cox regression-based, kidney donor risk index. A machine learning method may provide improved prediction of transplant outcomes and help decision-making. A popular tree-based machine learning method, random forest, was trained and evaluated with the same data originally used to develop the risk index (70,242 observations from 1995-2005). The random forest successfully predicted an additional 2,148 transplants than the risk index with equal type II error rates of 10%. Predicted results were analyzed with follow-up survival outcomes up to 240 months after transplant using Kaplan-Meier analysis and confirmed that the random forest performed significantly better than the risk index (p<0.05). The random forest predicted significantly more successful and longer-surviving transplants than the risk index. Random forests and other machine learning models may improve transplant decisions.

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Zhang, Zhidong, Xiubin Zhu i Ding Liu. "Model of Gradient Boosting Random Forest Prediction". W 2022 IEEE International Conference on Networking, Sensing and Control (ICNSC). IEEE, 2022. http://dx.doi.org/10.1109/icnsc55942.2022.10004112.

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Joshi, Shreya, Rachana Ramesh i Shagufta Tahsildar. "A Bankruptcy Prediction Model Using Random Forest". W 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, 2018. http://dx.doi.org/10.1109/iccons.2018.8663128.

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Raut, Archana, Dipti Theng i Sarika Khandelwal. "Random Forest Regressor Model for Rainfall Prediction". W 2023 International Conference on New Frontiers in Communication, Automation, Management and Security (ICCAMS). IEEE, 2023. http://dx.doi.org/10.1109/iccams60113.2023.10526085.

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Wang, Danqin, i Xiaolong Zhang. "Mobile user stability prediction with Random Forest model". W 2014 International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 2014. http://dx.doi.org/10.1109/dsaa.2014.7058108.

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Mallahi, Imad El, Asmae Dlia, Jamal Riffi, Mohamed Adnane Mahraz i Hamid Tairi. "Prediction of Traffic Accidents using Random Forest Model". W 2022 International Conference on Intelligent Systems and Computer Vision (ISCV). IEEE, 2022. http://dx.doi.org/10.1109/iscv54655.2022.9806099.

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Wen, Zhang, Zhaorui Jiang i Yutong Nie. "Wordle Distribution Prediction Model Based on Random Forest". W 2023 IEEE 3rd International Conference on Data Science and Computer Application (ICDSCA). IEEE, 2023. http://dx.doi.org/10.1109/icdsca59871.2023.10393098.

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Raporty organizacyjne na temat "Random Forest predictive model"

Ogunbire, Abimbola, Panick Kalambay, Hardik Gajera i Srinivas Pulugurtha. Deep Learning, Machine Learning, or Statistical Models for Weather-related Crash Severity Prediction. Mineta Transportation Institute, grudzień 2023. http://dx.doi.org/10.31979/mti.2023.2320.

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Nearly 5,000 people are killed and more than 418,000 are injured in weather-related traffic incidents each year. Assessments of the effectiveness of statistical models applied to crash severity prediction compared to machine learning (ML) and deep learning techniques (DL) help researchers and practitioners know what models are most effective under specific conditions. Given the class imbalance in crash data, the synthetic minority over-sampling technique for nominal (SMOTE-N) data was employed to generate synthetic samples for the minority class. The ordered logit model (OLM) and the ordered probit model (OPM) were evaluated as statistical models, while random forest (RF) and XGBoost were evaluated as ML models. For DL, multi-layer perceptron (MLP) and TabNet were evaluated. The performance of these models varied across severity levels, with property damage only (PDO) predictions performing the best and severe injury predictions performing the worst. The TabNet model performed best in predicting severe injury and PDO crashes, while RF was the most effective in predicting moderate injury crashes. However, all models struggled with severe injury classification, indicating the potential need for model refinement and exploration of other techniques. Hence, the choice of model depends on the specific application and the relative costs of false negatives and false positives. This conclusion underscores the need for further research in this area to improve the prediction accuracy of severe and moderate injury incidents, ultimately improving available data that can be used to increase road safety.

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Zyphur, Michael. Dynamic Structural Equation Modeling in Mplus. Instats Inc., 2023. http://dx.doi.org/10.61700/aypvl8azm5nlr469.

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This seminar will show you how to model longitudinal panel data as a multilevel model with contemporaneous and lagged effects. This type of dynamic SEM (DSEM) allows separating the stable and unstable components of observed variables, offering advantages such as including lagged effects to assess predictive forms of causality, as well as random slopes and variances to reflect individual differences in effects and volatility. The seminar covers this with hands-on examples that you can apply in your research. An official Instats certificate of completion is provided and the seminar offers 2 ECTS Equivalent points for European PhD students.

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Liu, Hongrui, i Rahul Ramachandra Shetty. Analytical Models for Traffic Congestion and Accident Analysis. Mineta Transportation Institute, listopad 2021. http://dx.doi.org/10.31979/mti.2021.2102.

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In the US, over 38,000 people die in road crashes each year, and 2.35 million are injured or disabled, according to the statistics report from the Association for Safe International Road Travel (ASIRT) in 2020. In addition, traffic congestion keeping Americans stuck on the road wastes millions of hours and billions of dollars each year. Using statistical techniques and machine learning algorithms, this research developed accurate predictive models for traffic congestion and road accidents to increase understanding of the complex causes of these challenging issues. The research used US Accidents data consisting of 49 variables describing 4.2 million accident records from February 2016 to December 2020, as well as logistic regression, tree-based techniques such as Decision Tree Classifier and Random Forest Classifier (RF), and Extreme Gradient boosting (XG-boost) to process and train the models. These models will assist people in making smart real-time transportation decisions to improve mobility and reduce accidents.

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Meidani, Hadi, i Amir Kazemi. Data-Driven Computational Fluid Dynamics Model for Predicting Drag Forces on Truck Platoons. Illinois Center for Transportation, listopad 2021. http://dx.doi.org/10.36501/0197-9191/21-036.

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Fuel-consumption reduction in the truck industry is significantly beneficial to both energy economy and the environment. Although estimation of drag forces is required to quantify fuel consumption of trucks, computational fluid dynamics (CFD) to meet this need is expensive. Data-driven surrogate models are developed to mitigate this concern and are promising for capturing the dynamics of large systems such as truck platoons. In this work, we aim to develop a surrogate-based fluid dynamics model that can be used to optimize the configuration of trucks in a robust way, considering various uncertainties such as random truck geometries, variable truck speed, random wind direction, and wind magnitude. Once trained, such a surrogate-based model can be readily employed for platoon-routing problems or the study of pavement performance.

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Pompeu, Gustavo, i José Luiz Rossi. Real/Dollar Exchange Rate Prediction Combining Machine Learning and Fundamental Models. Inter-American Development Bank, wrzesień 2022. http://dx.doi.org/10.18235/0004491.

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The study of the predictability of exchange rates has been a very recurring theme on the economics literature for decades, and very often is not possible to beat a random walk prediction, particularly when trying to forecast short time periods. Although there are several studies about exchange rate forecasting in general, predictions of specifically Brazilian real (BRL) to United States dollar (USD) exchange rates are very hard to find in the literature. The objective of this work is to predict the specific BRL to USD exchange rates by applying machine learning models combined with fundamental theories from macroeconomics, such as monetary and Taylor rule models, and compare the results to those of a random walk model by using the root mean squared error (RMSE) and the Diebold-Mariano (DM) test. We show that it is possible to beat the random walk by these metrics.

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White, Michael J., i Michelle E. Swearingen. Sound Propagation Through a Forest: A Predictive Model. Fort Belvoir, VA: Defense Technical Information Center, listopad 2004. http://dx.doi.org/10.21236/ada428938.

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Li, Yuan, Benjamin Metcalf, Sopio Chochua, Zhongya Li, Robert Gertz, Hollis Walker, Paulina Hawkins, Theresa Tran, Lesley McGee i 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.), listopad 2017. http://dx.doi.org/10.15620/cdc/147467.

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The datafiles, R scripts, MIC tables, and other files were used to evaluate the prediction performance of a penicillin-binding protein (PBP) typing system and two methods (Random Forest (RF) and Mode MIC (MM) previously developed by this research team. This data and these files support the finding of the paper "Validation of β-lactam minimum inhibitory concentration predictions for pneumococcal isolates with newly encountered penicillin binding protein (PBP) sequences" at https://doi.org/10.1186%2Fs12864-017-4017-7 or at https://stacks.cdc.gov/view/cdc/47684.

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Puttanapong, Nattapong, Arturo M. Martinez Jr, Mildred Addawe, Joseph Bulan, Ron Lester Durante i Marymell Martillan. Predicting Poverty Using Geospatial Data in Thailand. Asian Development Bank, grudzień 2020. http://dx.doi.org/10.22617/wps200434-2.

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This study examines an alternative approach in estimating poverty by investigating whether readily available geospatial data can accurately predict the spatial distribution of poverty in Thailand. It also compares the predictive performance of various econometric and machine learning methods such as generalized least squares, neural network, random forest, and support vector regression. Results suggest that intensity of night lights and other variables that approximate population density are highly associated with the proportion of population living in poverty. The random forest technique yielded the highest level of prediction accuracy among the methods considered, perhaps due to its capability to fit complex association structures even with small and medium-sized datasets.

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Rossi, Jose Luiz, Carlos Piccioni, Marina Rossi i Daniel Cuajeiro. Brazilian Exchange Rate Forecasting in High Frequency. Inter-American Development Bank, wrzesień 2022. http://dx.doi.org/10.18235/0004488.

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We investigated the predictability of the Brazilian exchange rate at High Frequency (1, 5 and 15 minutes), using local and global economic variables as predictors. In addition to the Linear Regression method, we use Machine Learning algorithms such as Ridge, Lasso, Elastic Net, Random Forest and Gradient Boosting. When considering contemporary predictors, it is possible to outperform the Random Walk at all frequencies, with local economic variables having greater predictive power than global ones. Machine Learning methods are also capable of reducing the mean squared error. When we consider only lagged predictors, it is possible to beat the Random Walk if we also consider the Brazilian Real futures as an additional predictor, for the frequency of one minute and up to two minutes ahead, confirming the importance of the Brazilian futures market in determining the spot exchange rate.

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Vas, Dragos, Elizabeth Corriveau, Lindsay Gaimaro i 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.), grudzień 2023. http://dx.doi.org/10.21079/11681/48018.

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Subarctic and Arctic environments are sensitive to warming temperatures due to climate change. As soils warm, soil microorganisms break down carbon and release greenhouse gases such as methane (CH₄) and carbon dioxide (CO₂). Recent studies examining CO₂ efflux note heterogeneity of microbial activity across the landscape. To better understand carbon dynamics, our team developed a predictive model, Dynamic Representation of Terrestrial Soil Predictions of Organisms’ Response to the Environment (DRTSPORE), to estimate CO₂ efflux based on soil temperature and moisture estimates. The goal of this work was to acquire respiration rates from a boreal forest located near the town of Fairbanks, Alaska, and to provide in situ measurements for the future validation effort of the DRTSPORE model estimates of CO₂ efflux in cold climates. Results show that soil temperature and seasonal soil thaw depth had the greatest impact on soil respiration. However, the instrumentation deployed significantly altered the soil temperature, moisture, and seasonal thaw depth at the survey site and very likely the soil respiration rates. These findings are important to better understand the challenges and limitations associated with the in situ data collection used for carbon efflux modeling and for estimating soil microbial activity in cold environments.

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