Academic literature on the topic 'XGBOOST PREDICTION MODEL'

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Journal articles on the topic "XGBOOST PREDICTION MODEL"

1

Zhao, Haolei, Yixian Wang, Xian Li, Panpan Guo, and Hang Lin. "Prediction of Maximum Tunnel Uplift Caused by Overlying Excavation Using XGBoost Algorithm with Bayesian Optimization." Applied Sciences 13, no. 17 (2023): 9726. http://dx.doi.org/10.3390/app13179726.

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The uplifting behaviors of existing tunnels due to overlying excavations are complex and non-linear. They are contributed to by multiple factors, and therefore, they are difficult to be accurately predicted. To address this issue, an extreme gradient boosting (XGBoost) prediction model based on Bayesian optimization (BO), namely, BO-XGBoost, was developed specifically for assessing the tunnel uplift. The modified model incorporated various factors such as an engineering design, soil types, and site construction conditions as input parameters. The performance of the BO-XGBoost model was compared with other models such as support vector machines (SVMs), the classification and regression tree (CART) model, and the extreme gradient boosting (XGBoost) model. In preparation for the model, 170 datasets from a construction site were collected and divided into 70% for training and 30% for testing. The BO-XGBoost model demonstrated a superior predictive performance, providing the most accurate displacement predictions and exhibiting better generalization capabilities. Further analysis revealed that the accuracy of the BO-XGBoost model was primarily influenced by the site’s construction factors. The interpretability of the BO-XGBoost model will provide valuable guidance for geotechnical practitioners in their decision-making processes.
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Gu, Xinqin, Li Yao, and Lifeng Wu. "Prediction of Water Carbon Fluxes and Emission Causes in Rice Paddies Using Two Tree-Based Ensemble Algorithms." Sustainability 15, no. 16 (2023): 12333. http://dx.doi.org/10.3390/su151612333.

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Quantification of water carbon fluxes in rice paddies and analysis of their causes are essential for agricultural water management and carbon budgets. In this regard, two tree-based machine learning models, which are extreme gradient boosting (XGBoost) and random forest (RF), were constructed to predict evapotranspiration (ET), net ecosystem carbon exchange (NEE), and methane flux (FCH4) in seven rice paddy sites. During the training process, the k-fold cross-validation algorithm by splitting the available data into multiple subsets or folds to avoid overfitting, and the XGBoost model was used to assess the importance of input factors. When predicting ET, the XGBoost model outperformed the RF model at all sites. Solar radiation was the most important input to ET predictions. Except for the KR-CRK site, the prediction for NEE was that the XGBoost models also performed better in the other six sites, and the root mean square error decreased by 0.90–11.21% compared to the RF models. Among all sites (except for the absence of net radiation (NETRAD) data at the JP-Mse site), NETRAD and normalized difference vegetation index (NDVI) performed well for predicting NEE. Air temperature, soil water content (SWC), and longwave radiation were particularly important at individual sites. Similarly, the XGBoost model was more capable of predicting FCH4 than the RF model, except for the IT-Cas site. FCH4 sensitivity to input factors varied from site to site. SWC, ecosystem respiration, NDVI, and soil temperature were important for FCH4 prediction. It is proposed to use the XGBoost model to model water carbon fluxes in rice paddies.
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Liu, Jialin, Jinfa Wu, Siru Liu, Mengdie Li, Kunchang Hu, and Ke Li. "Predicting mortality of patients with acute kidney injury in the ICU using XGBoost model." PLOS ONE 16, no. 2 (2021): e0246306. http://dx.doi.org/10.1371/journal.pone.0246306.

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Purpose The goal of this study is to construct a mortality prediction model using the XGBoot (eXtreme Gradient Boosting) decision tree model for AKI (acute kidney injury) patients in the ICU (intensive care unit), and to compare its performance with that of three other machine learning models. Methods We used the eICU Collaborative Research Database (eICU-CRD) for model development and performance comparison. The prediction performance of the XGBoot model was compared with the other three machine learning models. These models included LR (logistic regression), SVM (support vector machines), and RF (random forest). In the model comparison, the AUROC (area under receiver operating curve), accuracy, precision, recall, and F1 score were used to evaluate the predictive performance of each model. Results A total of 7548 AKI patients were analyzed in this study. The overall in-hospital mortality of AKI patients was 16.35%. The best performing algorithm in this study was XGBoost with the highest AUROC (0.796, p < 0.01), F1(0.922, p < 0.01) and accuracy (0.860). The precision (0.860) and recall (0.994) of the XGBoost model rank second among the four models. Conclusion XGBoot model had obvious advantages of performance compared to the other machine learning models. This will be helpful for risk identification and early intervention for AKI patients at risk of death.
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Wang, Jun, Wei Rong, Zhuo Zhang, and Dong Mei. "Credit Debt Default Risk Assessment Based on the XGBoost Algorithm: An Empirical Study from China." Wireless Communications and Mobile Computing 2022 (March 19, 2022): 1–14. http://dx.doi.org/10.1155/2022/8005493.

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The bond market is an important part of China’s capital market. However, defaults have become frequent in the bond market in recent years, and consequently, the default risk of Chinese credit bonds has become increasingly prominent. Therefore, the assessment of default risk is particularly important. In this paper, we utilize 31 indicators at the macroeconomic level and the corporate microlevel for the prediction of bond defaults, and we conduct principal component analysis to extract 10 principal components from them. We use the XGBoost algorithm to analyze the importance of variables and assess the credit debt default risk based on the XGBoost prediction model through the calculation of evaluation indicators such as the area under the ROC curve (AUC), accuracy, precision, recall, and F1-score, in order to evaluate the classification prediction effect of the model. Finally, the grid search algorithm and k -fold cross-validation are used to optimize the parameters of the XGBoost model and determine the final classification prediction model. Existing research has focused on the selection of bond default risk prediction indicators and the application of XGBoost algorithm in default risk prediction. After optimization of the parameters, the optimized XGBoost algorithm is found to be more accurate than the original algorithm. The grid search and k -fold cross-validation algorithms are used to optimize the XGBoost model for predicting the default risk of credit bonds, resulting in higher accuracy of the proposed model. Our research results demonstrate that the optimized XGBoost model has a significantly improved prediction accuracy, compared to the original model, which is beneficial to improving the prediction effect for practical applications.
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Gu, Zhongyuan, Miaocong Cao, Chunguang Wang, Na Yu, and Hongyu Qing. "Research on Mining Maximum Subsidence Prediction Based on Genetic Algorithm Combined with XGBoost Model." Sustainability 14, no. 16 (2022): 10421. http://dx.doi.org/10.3390/su141610421.

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The extreme gradient boosting (XGBoost) ensemble learning algorithm excels in solving complex nonlinear relational problems. In order to accurately predict the surface subsidence caused by mining, this work introduces the genetic algorithm (GA) and XGBoost integrated algorithm model for mining subsidence prediction and uses the Python language to develop the GA-XGBoost combined model. The hyperparameter vector of XGBoost is optimized by a genetic algorithm to improve the prediction accuracy and reliability of the XGBoost model. Using some domestic mining subsidence data sets to conduct a model prediction evaluation, the results show that the R2 (coefficient of determination) of the prediction results of the GA-XGBoost model is 0.941, the RMSE (root mean square error) is 0.369, and the MAE (mean absolute error) is 0.308. Then, compared with classic ensemble learning models such as XGBoost, random deep forest, and gradient boost, the GA-XGBoost model has higher prediction accuracy and performance than a single machine learning model.
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Kang, Leilei, Guojing Hu, Hao Huang, Weike Lu, and Lan Liu. "Urban Traffic Travel Time Short-Term Prediction Model Based on Spatio-Temporal Feature Extraction." Journal of Advanced Transportation 2020 (August 14, 2020): 1–16. http://dx.doi.org/10.1155/2020/3247847.

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In order to improve the accuracy of short-term travel time prediction in an urban road network, a hybrid model for spatio-temporal feature extraction and prediction of urban road network travel time is proposed in this research, which combines empirical dynamic modeling (EDM) and complex networks (CN) with an XGBoost prediction model. Due to the highly nonlinear and dynamic nature of travel time series, it is necessary to consider time dependence and the spatial reliance of travel time series for predicting the travel time of road networks. The dynamic feature of the travel time series can be revealed by the EDM method, a nonlinear approach based on Chaos theory. Further, the spatial characteristic of urban traffic topology can be reflected from the perspective of complex networks. To fully guarantee the reasonability and validity of spatio-temporal features, which are dug by empirical dynamic modeling and complex networks (EDMCN), for urban traffic travel time prediction, an XGBoost prediction model is established for those characteristics. Through the in-depth exploration of the travel time and topology of a particular road network in Guiyang, the EDMCN-XGBoost prediction model’s performance is verified. The results show that, compared with the single XGBoost, autoregressive moving average, artificial neural network, support vector machine, and other models, the proposed EDMCN-XGBoost prediction model presents a better performance in forecasting.
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Wang, Wenle, Wentao Xiong, Jing Wang, et al. "A User Purchase Behavior Prediction Method Based on XGBoost." Electronics 12, no. 9 (2023): 2047. http://dx.doi.org/10.3390/electronics12092047.

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With the increasing use of electronic commerce, online purchasing users have been rapidly rising. Predicting user behavior has therefore become a vital issue based on the collected data. However, traditional machine learning algorithms for prediction require significant computing time and often produce unsatisfactory results. In this paper, a prediction model based on XGBoost is proposed to predict user purchase behavior. Firstly, a user value model (LDTD) utilizing multi-feature fusion is proposed to differentiate between user types based on the available user account data. The multi-feature behavior fusion is carried out to generate the user tag feature according to user behavior patterns. Next, the XGBoost feature importance model is employed to analyze multi-dimensional features and identify the model with the most significant weight value as the key feature for constructing the model. This feature, together with other user features, is then used for prediction via the XGBoost model. Compared to existing machine learning models such as K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), and Back Propagation Neural Network (BPNN), the eXtreme Gradient Boosting (XGBoost) model outperforms with an accuracy of 0.9761, an F1 score of 0.9763, and a ROC value of 0.9768. Thus, the XGBoost model demonstrates superior stability and algorithm efficiency, making it an ideal choice for predicting user purchase behavior with high levels of accuracy.
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8

Oubelaid, Adel, Abdelhameed Ibrahim, and Ahmed M. Elshewey. "Bridging the Gap: An Explainable Methodology for Customer Churn Prediction in Supply Chain Management." Journal of Artificial Intelligence and Metaheuristics 4, no. 1 (2023): 16–23. http://dx.doi.org/10.54216/jaim.040102.

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Customer churn prediction is a critical task for businesses aiming to retain their valuable customers. Nevertheless, the lack of transparency and interpretability in machine learning models hinders their implementation in real-world applications. In this paper, we introduce a novel methodology for customer churn prediction in supply chain management that addresses the need for explainability. Our approach take advantage of XGBoost as the underlying predictive model. We recognize the importance of not only accurately predicting churn but also providing actionable insights into the key factors driving customer attrition. To achieve this, we employ Local Interpretable Model-agnostic Explanations (LIME), a state-of-the-art technique for generating intuitive and understandable explanations. By utilizing LIME to the predictions made by XGBoost, we enable decision-makers to gain intuition into the decision process of the model and the reasons behind churn predictions. Through a comprehensive case study on customer churn data, we demonstrate the success of our explainable ML approach. Our methodology not only achieves high prediction accuracy but also offers interpretable explanations that highlight the underlying drivers of customer churn. These insights supply valuable management for decision-making processes within supply chain management.
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9

Liu, Yuan, Wenyi Du, Yi Guo, Zhiqiang Tian, and Wei Shen. "Identification of high-risk factors for recurrence of colon cancer following complete mesocolic excision: An 8-year retrospective study." PLOS ONE 18, no. 8 (2023): e0289621. http://dx.doi.org/10.1371/journal.pone.0289621.

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Background Colon cancer recurrence is a common adverse outcome for patients after complete mesocolic excision (CME) and greatly affects the near-term and long-term prognosis of patients. This study aimed to develop a machine learning model that can identify high-risk factors before, during, and after surgery, and predict the occurrence of postoperative colon cancer recurrence. Methods The study included 1187 patients with colon cancer, including 110 patients who had recurrent colon cancer. The researchers collected 44 characteristic variables, including patient demographic characteristics, basic medical history, preoperative examination information, type of surgery, and intraoperative information. Four machine learning algorithms, namely extreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM), and k-nearest neighbor algorithm (KNN), were used to construct the model. The researchers evaluated the model using the k-fold cross-validation method, ROC curve, calibration curve, decision curve analysis (DCA), and external validation. Results Among the four prediction models, the XGBoost algorithm performed the best. The ROC curve results showed that the AUC value of XGBoost was 0.962 in the training set and 0.952 in the validation set, indicating high prediction accuracy. The XGBoost model was stable during internal validation using the k-fold cross-validation method. The calibration curve demonstrated high predictive ability of the XGBoost model. The DCA curve showed that patients who received interventional treatment had a higher benefit rate under the XGBoost model. The external validation set’s AUC value was 0.91, indicating good extrapolation of the XGBoost prediction model. Conclusion The XGBoost machine learning algorithm-based prediction model for colon cancer recurrence has high prediction accuracy and clinical utility.
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He, Wenwen, Hongli Le, and Pengcheng Du. "Stroke Prediction Model Based on XGBoost Algorithm." International Journal of Applied Sciences & Development 1 (December 13, 2022): 7–10. http://dx.doi.org/10.37394/232029.2022.1.2.

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In this paper, individual sample data randomly measured are preprocessed, for example, outliers values are deleted and the characteristics of the samples are normalized to between 0 and 1. The correlation analysis approach is then used to determine and rank the relevance of stroke characteristics, and factors with poor correlation are discarded. The samples are randomly split into a 70% training set and a 30% testing set. Finally,the random forest model and XGBoost algorithm combined with cross-validation and grid search method are implemented to learn the stroke characteristics. The accuracy of the testing set by the XGBoost algorithm is 0.9257, which is better than that of the random forest model with 0.8991. Thus, the XGBoost model is selected to predict the stroke for ten people, and the obtained conclusion is that two people have a stroke and eight people have no stroke.
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