IEEE

Enhancing Prediction Accuracy Through Random Forest in Classification and Regression

Abstract: Random Forest is a well-known type of ensemble learning, which combines a number of decision trees to improve the prediction ability and reduce the risk of overfitting. This paper aims at ...
Frontiers

Enhanced risk prediction of femoral head osteonecrosis in the elderly: a comparative study of random forest and logistic regression models

Department of Orthopedics, Shanxi Bethune Hospital, Tongji Shanxi Hospital, Shanxi Academy of Medical Sciences, Third Hospital of Shanxi Medical University, Taiyuan, China Background and aim: ...
IEEE

Evaluating the Performance of Random Forest Regression in Predicting Electric Motor Temperature

Objective: This research applies Random Forest Regression(RFR) as a non-linear technique to forecast temperature based on multiple input parameters, including motor load, speed, and ambient ...
Frontiers

Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China

Accurately quantifying forest volume and identifying its driving mechanisms are critical for achieving carbon neutrality objectives. Using data from the National Forest Inventory (NFI), plot-level ...