Global ETD Search

1. 1

   Kredito rizikos prognozavimas naudojantis mašininio mokymosi algoritmu ,,XGboost" /

   Norkus, Domantas,Institutional Repository of Vilnius University2020

   … I will introduce machine learning algorithm „XGboost” which will try to predict rating downgrade. Attention will be drawn to algorithm’s theoretical background, analysing how regression and regularization functions behave in tree space. Thus, methods for analysing model performence will be …

   vilnius Repository record for Kredito rizikos prognozavimas naudojantis mašininio mokymosi algoritmu ,,XGboost" / (opens in a new tab)

2. 2

   Forecasting U.S. county drug poisoning mortality rates through Linear Regression, XGBoost, and LightGBM : utilizing Linear Regression, XGBoost, and LightGBM for comparative time series analysis with exogenous variables

   Manuelian, H. der2025

   tilburg Repository record for Forecasting U.S. county drug poisoning mortality rates through Linear Regression, XGBoost, and LightGBM : utilizing Linear Regression, XGBoost, and LightGBM for comparative time series analysis with exogenous variables (opens in a new tab)

3. 3

   Forcasting board game sales using TabNet and XGBoost with tabular and text data

   Eng, M. van den2024

   tilburg Repository record for Forcasting board game sales using TabNet and XGBoost with tabular and text data (opens in a new tab)

4. 4

   Enhancing loan default prediction: a comparative analysis of XGBoost and TabNet with interpretability

   Wang, K.2024

   tilburg Repository record for Enhancing loan default prediction: a comparative analysis of XGBoost and TabNet with interpretability (opens in a new tab)

5. 5

   Using recency, frequency and monetary variables to predict customer lifetime value with XGBoost

   Myburg, Marius ErrolDepartment of Computer Science2023

   … machine learning models constructed with XGBoost to predict future CLV, as well as the likelihood that a customer will drop to a lower CLV in the future. One approach to determining CLV, called the RFM method, is done by isolating recency (R), frequency (F) and (M) monetary values. The …

   cape-town Repository record for Using recency, frequency and monetary variables to predict customer lifetime value with XGBoost (opens in a new tab)

6. 6

   Predicting general psychopathology: integrating fMRI and behavioral data using Random Forests, XGBoost, and ensemble models

   Fleischeuer, T.2025

   tilburg Repository record for Predicting general psychopathology: integrating fMRI and behavioral data using Random Forests, XGBoost, and ensemble models (opens in a new tab)

7. 7

   Predicting financial crime : a comparative case study on Random Forests, XGBoost and class imbalance techniques

   Bosmans, Y.H.P.2024

   tilburg Repository record for Predicting financial crime : a comparative case study on Random Forests, XGBoost and class imbalance techniques (opens in a new tab)

8. 8

   Forecasting perishable food demand using Random Forest and XGBoost: a comparative study of time-series analysis

   Tang, H.2024

   tilburg Repository record for Forecasting perishable food demand using Random Forest and XGBoost: a comparative study of time-series analysis (opens in a new tab)

9. 9

   Predicting earthquake damage: XGBoost vs. TabNet : and measuring the impact of synthethic sampling for tackling class imbalance

   Kantar, A.2025

   tilburg Repository record for Predicting earthquake damage: XGBoost vs. TabNet : and measuring the impact of synthethic sampling for tackling class imbalance (opens in a new tab)

10. 10

    Machine learning models for predicting energy consumption in a logistic warehouse : a focus on hybrid LSTM-XGBoost models

    Shen, W.2024

    tilburg Repository record for Machine learning models for predicting energy consumption in a logistic warehouse : a focus on hybrid LSTM-XGBoost models (opens in a new tab)

11. 11

    Implementation of Naïve Bayes, MLP, and XGBoost algorithms in a data stream setup for predicting hotel booking cancellations

    Ye, K.2024

    tilburg Repository record for Implementation of Naïve Bayes, MLP, and XGBoost algorithms in a data stream setup for predicting hotel booking cancellations (opens in a new tab)

12. 12

    Time-series prediction of air pollution with machine learning: a comparative study of XGBoost, Random Forest, and LSTM models

    Wegner, J.2024

    tilburg Repository record for Time-series prediction of air pollution with machine learning: a comparative study of XGBoost, Random Forest, and LSTM models (opens in a new tab)

13. 13

    Impact of metadata on civil and military aircraft classification in satellite imagery : a multi-modal framework with YOLO and XGBoost

    Tandoğan, T.2025

    tilburg Repository record for Impact of metadata on civil and military aircraft classification in satellite imagery : a multi-modal framework with YOLO and XGBoost (opens in a new tab)

14. 14

    Predicting job satisfaction using XGBoost, Random Forest and Ordinal Logistic Regression on longitudinal survey data : a study of young Dutch workers

    Krijnen, S.2025

    tilburg Repository record for Predicting job satisfaction using XGBoost, Random Forest and Ordinal Logistic Regression on longitudinal survey data : a study of young Dutch workers (opens in a new tab)

15. 15

    Decoding news credibility : using structural webdata from HTML and CSS code to predict credibility of news websites with Random Forest and XGBoost

    Welter, S.2025

    tilburg Repository record for Decoding news credibility : using structural webdata from HTML and CSS code to predict credibility of news websites with Random Forest and XGBoost (opens in a new tab)

16. 16

    Classifying mental health outcomes using the MHI-5 Screening Tool : a comparative study of Random Forest, XGBoost, Support Vector Machine and Logistic Regression

    Cosster, N.R.C.2025

    tilburg Repository record for Classifying mental health outcomes using the MHI-5 Screening Tool : a comparative study of Random Forest, XGBoost, Support Vector Machine and Logistic Regression (opens in a new tab)

17. 17

    Integrating restaurant features and user reviews to predict ratings : a cross region study combining Convolutional Neural Networks (CNN) and Extreme Gradient Boosting (XGBoost) models

    Pan, X.2025

    tilburg Repository record for Integrating restaurant features and user reviews to predict ratings : a cross region study combining Convolutional Neural Networks (CNN) and Extreme Gradient Boosting (XGBoost) models (opens in a new tab)

18. 18

    Predicting sustainable policy action plan performance of local governments : a comparative study on Multiple Linear Regression, Support Vector Regressor, Random Forest Regressor and XGBoost

    Verton, R.W.2024

    tilburg Repository record for Predicting sustainable policy action plan performance of local governments : a comparative study on Multiple Linear Regression, Support Vector Regressor, Random Forest Regressor and XGBoost (opens in a new tab)

19. 19

    Predicting student performance on a reading test in 2022 based on data from 2015 and 2018 using machine learning models: Random Forest, XGBoost, and AdaBoost

    Ginkel, M. van2024

    tilburg Repository record for Predicting student performance on a reading test in 2022 based on data from 2015 and 2018 using machine learning models: Random Forest, XGBoost, and AdaBoost (opens in a new tab)

20. 20

    Racing against the clock: predicting Hyrox finish times with demographic and split race data : a comparative study using Multiple Linear Regression, Random Forest, and XGBoost

    Moerenhout, Q.2025

    tilburg Repository record for Racing against the clock: predicting Hyrox finish times with demographic and split race data : a comparative study using Multiple Linear Regression, Random Forest, and XGBoost (opens in a new tab)

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