TreeMap 2016 Stand Size Code Algorithm (Image Service) حسب U.S. Forest Service (17476914)

"…<div>TreeMap 2016 provides a tree-level model of the forests of the conterminous United States.…"

Improved random forest algorithm. حسب Zhen Zhao (159931)

"…Compared with MDA-RF, the prediction accuracy of the improved RF built on the same subset increased by 1.7%, indicating that improving the bootstrap sampling of random forest by using the K-means++ clustering algorithm can enhance model accuracy to some extent. …"

Random Forest and Gradient Boosting algorithms. حسب Yvan J. Garcia-Lopez (20783591)

Schematic diagram of random forest algorithm. حسب Imee V. Necesito (12749885)

Pseudocode for the missForestPredict algorithm. حسب Elena Albu (15181070)

"…Missing data in input variables often occur at model development and at prediction time. The missForestPredict R package proposes an adaptation of the missForest imputation algorithm that is fast, user-friendly and tailored for prediction settings. …"

The schematic diagram of the iForest algorithm. حسب Zhibo Xie (6790775)

الموضوعات:

Prediction performance compared with random forest algorithm. حسب Yixian Wen (12201388)

The basic process of feature selection of the random forest algorithm. حسب Jintao Li (448681)

Predictive variables screened by LASSO regression and Random Forest algorithm. حسب Da-cheng Wang (22232009)

الموضوعات:

The algorithm of the applied models. حسب Elif Tuçe Bal (21367965)

الموضوعات:

Pseudocode for the missForestPredict algorithm - new observation. حسب Elena Albu (15181070)

Main code. حسب Jingguo Bi (21439766)

General model performance for different algorithms. حسب Binghan Xue (21253235)

الموضوعات:

Performance of models by different algorithms. حسب Yong Zhang (5893)

الموضوعات:

Genetic algorithm flowchart. حسب Wenguang Li (6528113)

الموضوعات:

Random forest model details. حسب Giuliano Lorenzoni (21440268)

الموضوعات:

Results of Random Forest. حسب Balraj Preet Kaur (20370832)

Image 9_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.jpeg حسب Junfei Zhang (7547975)

"…Using data from the National Forest Inventory (NFI), plot-level measurements, and environmental variables from pure larch (LP), birch (BP), and mixed larch-birch (LB) forests in the mountainous region of northern Hebei, China, this study employed random forest (RF) algorithms to evaluate the relative importance and partial dependence of biotic and abiotic factors on stand volume growth. …"

Table 1_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.docx حسب Junfei Zhang (7547975)

"…Using data from the National Forest Inventory (NFI), plot-level measurements, and environmental variables from pure larch (LP), birch (BP), and mixed larch-birch (LB) forests in the mountainous region of northern Hebei, China, this study employed random forest (RF) algorithms to evaluate the relative importance and partial dependence of biotic and abiotic factors on stand volume growth. …"

Image 10_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.jpeg حسب Junfei Zhang (7547975)

"…Using data from the National Forest Inventory (NFI), plot-level measurements, and environmental variables from pure larch (LP), birch (BP), and mixed larch-birch (LB) forests in the mountainous region of northern Hebei, China, this study employed random forest (RF) algorithms to evaluate the relative importance and partial dependence of biotic and abiotic factors on stand volume growth. …"