Journal: PLOS ONE

Article Title: Comparing machine learning methods for predicting land development intensity

doi: 10.1371/journal.pone.0282476

Figure Lengend Snippet: Parameter comparison of the maximum prediction.

Article Snippet: The XGBOOST algorithm has proven its effectiveness in numerous machine learning and data mining challenges for prediction and classification problems and was rated as the top solution algorithm in a machine learning competition held on the Kaggle website [ ], with the main advantages being the minimum requirement for attribute normalization, intelligent handling of missing values, and providing solutions that avoid overfitting [ , ].

Techniques: Comparison, Plasmid Preparation

Journal: PLOS ONE

Article Title: Comparing machine learning methods for predicting land development intensity

doi: 10.1371/journal.pone.0282476

Figure Lengend Snippet: Modeling flowchart of XGBOOST based on land development intensity.

Article Snippet: The XGBOOST algorithm has proven its effectiveness in numerous machine learning and data mining challenges for prediction and classification problems and was rated as the top solution algorithm in a machine learning competition held on the Kaggle website [ ], with the main advantages being the minimum requirement for attribute normalization, intelligent handling of missing values, and providing solutions that avoid overfitting [ , ].

Techniques:

Journal: PLOS ONE

Article Title: Comparing machine learning methods for predicting land development intensity

doi: 10.1371/journal.pone.0282476

Figure Lengend Snippet: XGBOOST model parameters in the process of training the learning curve.

Article Snippet: The XGBOOST algorithm has proven its effectiveness in numerous machine learning and data mining challenges for prediction and classification problems and was rated as the top solution algorithm in a machine learning competition held on the Kaggle website [ ], with the main advantages being the minimum requirement for attribute normalization, intelligent handling of missing values, and providing solutions that avoid overfitting [ , ].

Techniques:

Journal: PLOS ONE

Article Title: Comparing machine learning methods for predicting land development intensity

doi: 10.1371/journal.pone.0282476

Figure Lengend Snippet: XGBOOST super parameter combination of the model.

Article Snippet: The XGBOOST algorithm has proven its effectiveness in numerous machine learning and data mining challenges for prediction and classification problems and was rated as the top solution algorithm in a machine learning competition held on the Kaggle website [ ], with the main advantages being the minimum requirement for attribute normalization, intelligent handling of missing values, and providing solutions that avoid overfitting [ , ].

Techniques:

Journal: PLOS ONE

Article Title: Comparing machine learning methods for predicting land development intensity

doi: 10.1371/journal.pone.0282476

Figure Lengend Snippet: XGBOOST model test set verification results description.

Article Snippet: The XGBOOST algorithm has proven its effectiveness in numerous machine learning and data mining challenges for prediction and classification problems and was rated as the top solution algorithm in a machine learning competition held on the Kaggle website [ ], with the main advantages being the minimum requirement for attribute normalization, intelligent handling of missing values, and providing solutions that avoid overfitting [ , ].

Techniques:

Journal: PLOS ONE

Article Title: Comparing machine learning methods for predicting land development intensity

doi: 10.1371/journal.pone.0282476

Figure Lengend Snippet: The fitting map of land development intensity test based on the XGBOOST model.

Article Snippet: The XGBOOST algorithm has proven its effectiveness in numerous machine learning and data mining challenges for prediction and classification problems and was rated as the top solution algorithm in a machine learning competition held on the Kaggle website [ ], with the main advantages being the minimum requirement for attribute normalization, intelligent handling of missing values, and providing solutions that avoid overfitting [ , ].

Techniques:

Journal: BMC Medical Informatics and Decision Making

Article Title: The advanced machine learner XGBoost did not reduce prehospital trauma mistriage compared with logistic regression: a simulation study

doi: 10.1186/s12911-021-01558-y

Figure Lengend Snippet: Under- and overtriage rates of logistic regression and XGBoost (median and 2.5 and 97.5 percentiles (calculation on 1000 runs))

Article Snippet: We chose the machine learner (XGBoost) because it has recently been dominating applied machine learning and Kaggle competitions [ , ].

Techniques:

Journal: BMC Medical Informatics and Decision Making

Article Title: The advanced machine learner XGBoost did not reduce prehospital trauma mistriage compared with logistic regression: a simulation study

doi: 10.1186/s12911-021-01558-y

Figure Lengend Snippet: Under- and overtriage rates (median, IQR (Q1-Q3), Q1-1,5IQR & Q3 + 1,5IQR) for logistic regression and XGBoost in the SweTrau and NTDB cohorts. NTDB, National Trauma Data Bank; SweTrau, Swedish Trauma Registry

Article Snippet: We chose the machine learner (XGBoost) because it has recently been dominating applied machine learning and Kaggle competitions [ , ].

Techniques:

Journal: BMC Medical Informatics and Decision Making

Article Title: The advanced machine learner XGBoost did not reduce prehospital trauma mistriage compared with logistic regression: a simulation study

doi: 10.1186/s12911-021-01558-y

Figure Lengend Snippet: Median and 2.5 and 97.5 percentiles of difference in under- and overtriage rates between learners (calculation on 1000 runs)

Article Snippet: We chose the machine learner (XGBoost) because it has recently been dominating applied machine learning and Kaggle competitions [ , ].

Techniques:

Journal: Scientific Reports

Article Title: Assessment and simulation of eco-environmental quality changes in rapid rural urbanization: Xiong’an New Area, China

doi: 10.1038/s41598-024-73487-5

Figure Lengend Snippet: The hyperparameters of the XGBoost algorithm used in this study and their corresponding tuning space.

Article Snippet: We constructed an XGBoost regression model using Rongcheng County’s 2021 RSEI as the dependent variable and five urban-rural construction features, i.e., NDVI nor , NDISI nor , PB, PopD, and BH, as independent variables.

Techniques:

Journal: Scientific Reports

Article Title: Assessment and simulation of eco-environmental quality changes in rapid rural urbanization: Xiong’an New Area, China

doi: 10.1038/s41598-024-73487-5

Figure Lengend Snippet: Analysis results of the urban-rural construction features in the XGBoost regression model.

Article Snippet: We constructed an XGBoost regression model using Rongcheng County’s 2021 RSEI as the dependent variable and five urban-rural construction features, i.e., NDVI nor , NDISI nor , PB, PopD, and BH, as independent variables.

Techniques:

Journal: Journal of Personalized Medicine

Article Title: Dynomics: A Novel and Promising Approach for Improved Breast Cancer Prognosis Prediction

doi: 10.3390/jpm13061004

Figure Lengend Snippet: Radiomics Workflow. Beginning with the acquisition of medical images, volumes of interest (VOIs) are manually segmented on both the lesions and a healthy reference tissue. Radiomic features are subsequently extracted from these VOIs and superimposed onto the static 18 F-FLT PET image (a 3D image derived from averaging the final five time-frames of the dynamic acquisition), thus establishing the basis for static radiomics. For our novel dynomics approach, these features are extrapolated from each frame of the dynamic 18 F-FLT PET acquisition. In this process, summary values—including median and median absolute deviation (MAD)—are evaluated for each feature and analyzed in conjunction with their temporal evolution (dynamic features). The features encapsulate information about the tumor’s shape, first-order statistical features (derived from the image intensity histogram), and second-order statistical features (texture features). To optimize the data for interpretation, radiomics features undergo redundancy correction via principal component analysis (PCA), enabling the analysis of only non-redundant, meaningful features. These streamlined features are then processed through a machine learning model (XGBoost), generating a clinically interpretable outcome (lesion vs. reference tissue and complete vs. partial responders’ classification).

Article Snippet: , Dynomics—Median , XGBoost , 0.67 , 0.71 , 1.00 , 0.67 , 0.33 , 1.00.

Techniques: Derivative Assay

Journal: Journal of Personalized Medicine

Article Title: Dynomics: A Novel and Promising Approach for Improved Breast Cancer Prognosis Prediction

doi: 10.3390/jpm13061004

Figure Lengend Snippet: Summary of model performances when discriminating tumors from the reference tissue using static and dynamic radiomic features and images.

Article Snippet: , Dynomics—Median , XGBoost , 0.67 , 0.71 , 1.00 , 0.67 , 0.33 , 1.00.

Techniques:

Journal: Journal of Personalized Medicine

Article Title: Dynomics: A Novel and Promising Approach for Improved Breast Cancer Prognosis Prediction

doi: 10.3390/jpm13061004

Figure Lengend Snippet: Summary of model performance when discriminating complete from partial responders using static and dynamic radiomic features and images.

Article Snippet: , Dynomics—Median , XGBoost , 0.67 , 0.71 , 1.00 , 0.67 , 0.33 , 1.00.

Techniques:

Journal: Bioengineering

Article Title: Enhancing Cardiovascular Risk Prediction: Development of an Advanced Xgboost Model with Hospital-Level Random Effects

doi: 10.3390/bioengineering11101039

Figure Lengend Snippet: CEM and individual component metrics for Xgboost BME and NC variant models.

Article Snippet: The standardized Xgboost NC model demonstrated slightly higher performance (CEM 0.741: 95%CI: 0.7405–0.7411; ) than the other Xgboost model variants when all training data samples were utilized.

Techniques: Variant Assay

Journal: Bioengineering

Article Title: Enhancing Cardiovascular Risk Prediction: Development of an Advanced Xgboost Model with Hospital-Level Random Effects

doi: 10.3390/bioengineering11101039

Figure Lengend Snippet: Relationship between sample size and CEM for standardized Xgboost NC and unstandardized Xgboost BME models.

Article Snippet: The standardized Xgboost NC model demonstrated slightly higher performance (CEM 0.741: 95%CI: 0.7405–0.7411; ) than the other Xgboost model variants when all training data samples were utilized.

Techniques:

Journal: Bioengineering

Article Title: Enhancing Cardiovascular Risk Prediction: Development of an Advanced Xgboost Model with Hospital-Level Random Effects

doi: 10.3390/bioengineering11101039

Figure Lengend Snippet: Relationship between sample size and AUC for unstandardized Xgboost BME and standardized Xgboost NC models.

Article Snippet: The standardized Xgboost NC model demonstrated slightly higher performance (CEM 0.741: 95%CI: 0.7405–0.7411; ) than the other Xgboost model variants when all training data samples were utilized.

Techniques:

Journal: Bioengineering

Article Title: Enhancing Cardiovascular Risk Prediction: Development of an Advanced Xgboost Model with Hospital-Level Random Effects

doi: 10.3390/bioengineering11101039

Figure Lengend Snippet: Unstandardized Xgboost BME: random effects (a_i) across hospitals; the line at y = 0 can alternatively be considered as Odds Ratio = 1 if transformed from log odds, i.e., no effect on mortality.

Article Snippet: The standardized Xgboost NC model demonstrated slightly higher performance (CEM 0.741: 95%CI: 0.7405–0.7411; ) than the other Xgboost model variants when all training data samples were utilized.

Techniques: Transformation Assay

Journal: Bioengineering

Article Title: Enhancing Cardiovascular Risk Prediction: Development of an Advanced Xgboost Model with Hospital-Level Random Effects

doi: 10.3390/bioengineering11101039

Figure Lengend Snippet: Unstandardized Xgboost BME: GLL across different sample sizes.

Article Snippet: The standardized Xgboost NC model demonstrated slightly higher performance (CEM 0.741: 95%CI: 0.7405–0.7411; ) than the other Xgboost model variants when all training data samples were utilized.

Techniques: