Results 31 to 40 of about 72,345 (262)

Internal validation of the XGBoost model.

, 2023
(A) ROC curve of the XGBoost model for the training set. (B) ROC curve of the XGBoost model for the validation set. (C) ROC curve of the XGBoost model for the test set. (D) External validation of the XGBoost model.
Yuan Liu (88411), Yi Guo (35817), Zhiqiang Tian (195085), Wei Shen (149127), Wenyi Du (8458209)   +4 more
core   +1 more source

Comparative Analysis of Feature Selection Methods with XGBoost for Malware Detection on the Drebin Dataset

Jurnal Sisfokom
Malware, or malicious software, continues to evolve alongside increasing cyberattacks targeting individual devices and critical infrastructure. Traditional detection methods, such as signature-based detection, are often ineffective against new or ...
Ines Aulia Latifah, Fauzi Adi Rafrastara, Jevan Bintoro, Wildanil Ghozi, Waleed Mahgoub Osman   +4 more
doaj   +1 more source

A comparative analysis of Deep Neural Networks and Gradient Boosting Algorithms in long-term wind power forecasting [PDF]

Zbornik Radova: Elektrotehnički Institut "Nikola Tesla"
A vital step toward a sustainable future is the power grid's incorporation of renewable energy sources. Wind energy is significant because of its broad availability and minimal environmental impact.
Ivanović Luka, Milić Saša D., Sokolović Živko, Rakić Aleksandar   +3 more
doaj   +1 more source

Interpretable Machine Learning with SHAP and XGBoost for Lung Cancer Prediction Insights

Journal of Applied Informatics and Computing
Kanker paru-paru tetap menjadi salah satu penyebab kematian utama di seluruh dunia, dan deteksi dini melalui metode yang akurat dan andal sangat penting untuk meningkatkan prognosis pasien.
Taufik Kurniawan, Laily Hermawanti, Achmad Nuruddin Safriandono   +2 more
doaj   +1 more source

Relative Humidity Prediction using XGBoost Machine Learning Model, Case Study: Bajgah Climatological Station, Iran [PDF]

مهندسی منابع آب
given the prevalence of available data for only these two parameters in many parts of the country, various scenarios involving these parameters were studied. The best scenario for predicting relative humidity was obtained using the XGBoost model.
Reza Piraei, Ali Mohammadi, Seied Hosein Afzali   +2 more
doaj   +1 more source

A Two‐Stage Questionnaire and Actigraphy Screening for iRBD in a Multicenter Retrospective Cohort

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Isolated rapid‐eye‐movement sleep behavior disorder is a prodromal marker of synucleinopathies. However, most cases remain undiagnosed due to the insufficient predictive value of questionnaires and limited access to confirmatory video‐polysomnography. We assessed a two‐stage screening strategy combining a brief questionnaire on rapid‐
Caleb A. Massimi, Giorgio Ricciardiello Mejia, Andre Metzger, Kang Hyun Ryu, Salonee Marwaha, Eva Grzegorczyk, Li Zhou, Eliana Jacobs, Boris Gilyadov, Claudia Kunney, Liqhwa Ncube, Ankit Parekh, Emmanuel Mignot, Fanny M. Elahi, Joseph Winer, Kathleen Poston, Andreas Brink‐Kjaer, Emmanuel H. During   +17 more
wiley   +1 more source

Innovative Machine Learning Approach for Distinguishing Rheumatoid Arthritis and Osteoarthritis: Integrating Shapely Additive Explanations and Dendrograms

Journal of Scientific Innovation in Medicine
Background: Arthritis is a major healthcare issue and accurate diagnosis is important to treatment. Objective: The study aimed to identify and intuitively visualize feature importance of factors associated with osteoarthritis versus rheumatoid arthritis ...
Alexander A. Huang, Samuel Y. Huang
doaj   +1 more source

Thalamo‐Lesional Connectivity Signatures of Bilateral Tonic–Clonic Seizures in Focal Cortical Dysplasia‐Related Epilepsy

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objectives Focal cortical dysplasia (FCD) is the most common etiology of drug‐resistant epilepsy in children. Focal to bilateral tonic–clonic seizures (FBTCS) mark a high risk of drug‐resistant epilepsy and involve thalamocortical circuitry in their generation and propagation.
Hua Xie, Xiaotong Li, Venkata Sita Priyanka Illapani, Leigh N. Sepeta, Jason Schroeder, Chima O. Oluigbo, Madison M. Berl, William D. Gaillard, Nathan T. Cohen   +8 more
wiley   +1 more source

Multiple Imputation Through XGBoost

, 2023
The use of multiple imputation (MI) is becoming increasingly popular for addressing missing data. Although some conventional MI approaches have been well studied and have shown empirical validity, they have limitations when processing large datasets with
Deng, Yongshi, Lumley, Thomas
core   +3 more sources

Prediction results of XGBoost model.

, 2020
(A) XGBoost testing plot with Min-Max Accuracy 0.93, RMSE 2.57 and SD 3.97, (B) XGBoost training plot with Min-Max Accuracy 0.99, RMSE 0.02 and SD 3.97, (C) XGBoost test residual plot with Min-Max Accuracy 0.93, RMSE 2.57 and SD 3.97 (R package ggplot2).
Yuepeng Song (410775), Qing Fang (254280), Xiao-Yu Zhang (738362), Ziyuan Huang (4092580), Andrew Siu (8436678), Xuehui Su (8436675), Deqiang Zhang (12410)   +6 more
core   +1 more source