Machine Learning with XGboost by Lis Sulmont Live training sessions are designed to mimic the flow of how a real data scientist would address a problem or a task. As such, a session needs to have some ...

Machine learning algorithms, increasingly utilized in medical research (14), often outperform conventional statistical models. Recent applications in HICH include predicting hematoma expansion using ...

Four machine learning algorithms including extreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM) and k-nearest neighbor algorithm (KNN) were applied to construct the ...

Background: A pathophysiological interplay exists between plaque morphology and coronary physiology. Machine learning (ML) is increasingly being applied to coronary computed tomography angiography ...

Although the GA-XGBoost algorithm proposed in this paper achieves better results in terms of evaluation indexes compared with traditional machine learning algorithms, there are still shortcomings, as ...

The prediction model was built based on XGBoost algorithm, and it was compared with three other popular machine learning techniques: logistic regression, random forest and support vector machine. In ...